Compare commits
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Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 SolitaryThinker
|
14e334d0b4 |
@@ -61,7 +61,7 @@ steps:
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- "pyproject.toml"
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- "docker/Dockerfile.python3.12"
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config:
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command: "timeout 90m .buildkite/scripts/pr_test.sh"
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command: "timeout 60m .buildkite/scripts/pr_test.sh"
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label: "SSIM Tests"
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env:
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- TEST_TYPE=ssim
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@@ -156,23 +156,8 @@ jobs:
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# Fix the wheel to be manylinux compliant
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pip install auditwheel
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# Point auditwheel at torch libs, but do not vendor them into the wheel.
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TORCH_LIB_DIR=$(python - <<'PY'
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import os
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import torch
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print(os.path.join(os.path.dirname(torch.__file__), "lib"))
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PY
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)
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export LD_LIBRARY_PATH="${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}"
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# Target manylinux_2_35 (Ubuntu 22.04 native)
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auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist \
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--exclude libtorch_cuda.so \
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--exclude libtorch_cpu.so \
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--exclude libtorch.so \
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--exclude libc10.so \
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--exclude libc10_cuda.so \
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--exclude libtorch_python.so
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auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist
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# Move fixed wheels back to dist for upload consistency
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rm dist/*.whl
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mv fixed_dist/*.whl dist/
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@@ -68,7 +68,7 @@ repos:
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entry: bash
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args:
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- -c
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- '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'
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- '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'
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language: system
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always_run: true
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pass_filenames: false
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@@ -3,7 +3,7 @@
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</div>
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<p align="center">
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| <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> |
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| <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/c7g1qdD" target="_blank"> <b> WeChat </b> </a> |
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</p>
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**FastVideo is a unified post-training and inference framework for accelerated video generation.**
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Binary file not shown.
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Before Width: | Height: | Size: 490 KiB |
Binary file not shown.
@@ -41,7 +41,7 @@ Clone the repository and build the kernel:
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```bash
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# Clone recursively to get ThunderKittens submodule
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git clone https://github.com/hao-ai-lab/FastVideo.git
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git clone --recursive https://github.com/hao-ai-lab/FastVideo.git
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cd FastVideo/fastvideo-kernel
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# Build and install
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@@ -13,13 +13,11 @@ from fastvideo_kernel import video_sparse_attn
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# q, k, v: [batch_size, num_heads, seq_len, head_dim]
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# variable_block_sizes: Number of valid tokens per block
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# q_variable_block_sizes: Number of valid tokens per q block (can differ from KV for q/k of different lengths)
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# topk: Number of blocks to attend
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output = video_sparse_attn(
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q, k, v,
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block_sizes,
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block_sizes,
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variable_block_sizes=block_sizes,
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topk=32
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)
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```
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@@ -11,13 +11,15 @@ FastVideo supports the following hardware platforms:
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### Using pip
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```bash
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# Create and activate a new conda environment
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conda create -n fastvideo python=3.12
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conda activate fastvideo
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pip install fastvideo
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```
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### Using conda
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```bash
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conda install -c conda-forge fastvideo
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```
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### From source
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```bash
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@@ -26,12 +28,6 @@ cd FastVideo
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pip install -e .
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```
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Also optionally install flash-attn:
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```bash
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pip install flash-attn --no-build-isolation
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```
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## Hardware Requirements
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- **NVIDIA GPUs**: CUDA 11.8+ with compute capability 7.0+
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@@ -15,12 +15,6 @@ conda activate fastvideo
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pip install fastvideo
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```
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Also optionally install flash-attn:
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```bash
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pip install flash-attn --no-build-isolation
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```
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## Basic Usage
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### Text-to-Video Generation
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@@ -106,8 +106,6 @@ If you encounter CUDA out of memory errors:
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- Enable memory optimization with `enable_model_cpu_offload`
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- Try a smaller model or use distilled versions
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- Use `num_gpus` > 1 if multiple GPUs are available
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- Try enabling FSDP inference with `use_fsdp_inference=True` (may slow down generation)
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- Try enabling DiT layerwise offload with `dit_layerwise_offload=True` (now only a few models support this, but may introduce less overhead than FSDP)
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### Slow Generation
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@@ -1,71 +0,0 @@
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# LoRA Extraction and Merging
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Tools for extracting and merging LoRA adapters for FastVideo models.
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## Extract LoRA Adapter
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```bash
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python scripts/lora_extraction/extract_lora.py \
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--base Wan-AI/Wan2.2-TI2V-5B-Diffusers \
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--ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
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--out adapter_r32.safetensors \
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--rank 32
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```
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**Options:**
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- `--base`: Base model (HuggingFace ID or local path)
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- `--ft`: Fine-tuned model (HuggingFace ID or local path)
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- `--out`: Output adapter file
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- `--rank`: LoRA rank (16, 32, 64, 128)
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- `--full-rank`: Extract full-rank adapter (optional)
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## Merge Adapter
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```bash
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python scripts/lora_extraction/merge_lora.py \
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--base Wan-AI/Wan2.2-TI2V-5B-Diffusers \
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--adapter adapter_r32.safetensors \
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--ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
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--output merged_model
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```
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**Options:**
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- `--base`: Base model (HuggingFace ID or local path)
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- `--adapter`: LoRA adapter file (.safetensors)
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- `--ft`: Fine-tuned model (for configuration)
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- `--output`: Output directory
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## Validate Quality (Optional)
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```bash
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python scripts/lora_extraction/lora_inference_comparison.py \
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--base merged_model \
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--ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
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--adapter NONE \
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--output-dir results \
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--prompt "A cat sitting on a windowsill" \
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--seed 42 \
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--height 480 \
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--width 480 \
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--num-frames 49 \
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--num-inference-steps 32 \
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--compute-ssim \
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--compute-lpips
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```
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**Options:**
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- `--base`: Merged model or base model path
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- `--ft`: Fine-tuned model (reference)
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- `--adapter`: Path to adapter or NONE
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- `--output-dir`: Output directory
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- `--prompt`: Text prompt (default: "A cat sitting on a windowsill")
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- `--seed`: Random seed (default: 42)
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- `--height`: Video height (default: 480)
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- `--width`: Video width (default: 832)
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- `--num-frames`: Number of frames (default: 49)
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- `--num-inference-steps`: Inference steps (default: 32)
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- `--compute-ssim`: Compute SSIM metric
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- `--compute-lpips`: Compute LPIPS metric
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@@ -12,7 +12,7 @@ def main():
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"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
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# FastVideo will automatically handle distributed setup
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num_gpus=1,
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use_fsdp_inference=False, # set to True if GPU is out of memory
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use_fsdp_inference=True,
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dit_cpu_offload=False,
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vae_cpu_offload=False,
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text_encoder_cpu_offload=True,
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@@ -1,42 +0,0 @@
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from fastvideo import VideoGenerator
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def main():
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# Point this to your local diffusers model dir (or replace with a HF model ID).
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model_path = "KyleShao/Cosmos-Predict2.5-2B-Diffusers"
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generator = VideoGenerator.from_pretrained(
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model_path,
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num_gpus=1,
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use_fsdp_inference=False, # set True if GPU is out of memory
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dit_cpu_offload=False,
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vae_cpu_offload=False,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=True,
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)
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prompt = (
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"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."
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)
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video = generator.generate_video(
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prompt,
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negative_prompt="",
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height=704,
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width=1280,
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num_frames=77,
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num_inference_steps=35,
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guidance_scale=7.0,
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fps=24,
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output_path="outputs_video/cosmos2_5_t2w.mp4",
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save_video=True,
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)
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generator.shutdown()
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if __name__ == "__main__":
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main()
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@@ -14,13 +14,14 @@ def main():
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model_name,
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# FastVideo will automatically handle distributed setup
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num_gpus=1,
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use_fsdp_inference=False, # set to True if GPU is out of memory
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use_fsdp_inference=True,
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# Adjust these offload parameters if you have < 32GB of VRAM
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text_encoder_cpu_offload=True,
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pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
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dit_cpu_offload=False,
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vae_cpu_offload=False,
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VSA_sparsity=0.8,
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init_weights_from_safetensors="/mnt/weka/home/hao.zhang/wl/release/dmd_distill_1.3_4n_syn/checkpoint-900_weight_only/generator_inference_transformer"
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)
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load_end_time = time.perf_counter()
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load_time = load_end_time - load_start_time
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@@ -12,7 +12,7 @@ def main():
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"hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
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# FastVideo will automatically handle distributed setup
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num_gpus=1,
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use_fsdp_inference=False, # set to True if GPU is out of memory
|
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use_fsdp_inference=True,
|
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dit_cpu_offload=True,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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@@ -1,210 +0,0 @@
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"""
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LongCat Image-to-Video (I2V) Example Script
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This script demonstrates LongCat I2V inference using the FastVideo Python API.
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LongCat I2V takes an input image and generates a video from it.
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|
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It runs both basic generation (50 steps) and distill+refine generation
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(16 steps distill + 50 steps refinement to 720p with BSA).
|
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|
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Usage:
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python examples/inference/basic/basic_longcat_i2v.py
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|
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Note:
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Refinement uses 768x768 dimensions where latent (48x48) is divisible by 8,
|
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compatible with BSA chunks [4, 4, 8].
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"""
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import glob
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import os
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from fastvideo import VideoGenerator
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|
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# Common prompts and settings matching the shell script examples
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PROMPT = (
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"A woman sits at a wooden table by the window in a cozy café. She reaches out "
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"with her right hand, picks up the white coffee cup from the saucer, and gently "
|
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"brings it to her lips to take a sip. After drinking, she places the cup back on "
|
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"the table and looks out the window, enjoying the peaceful atmosphere."
|
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)
|
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|
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NEGATIVE_PROMPT = (
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"Bright tones, overexposed, static, blurred details, subtitles, style, works, "
|
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"paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
|
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"residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
|
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"deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
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"three legs, many people in the background, walking backwards"
|
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)
|
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|
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# Input image path
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IMAGE_PATH = "assets/girl.png"
|
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|
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SEED = 42
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|
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|
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def basic_generation():
|
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"""
|
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Run basic LongCat I2V generation (50 steps at 480p).
|
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|
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This uses the full 50-step denoising process for highest quality.
|
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"""
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print("=" * 60)
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print("LongCat I2V: Basic Generation (50 steps, 480p)")
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print("=" * 60)
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|
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generator = VideoGenerator.from_pretrained(
|
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"FastVideo/LongCat-Video-I2V-Diffusers",
|
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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"
|
||||
|
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generator.generate_video(
|
||||
prompt=PROMPT,
|
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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()
|
||||
|
||||
|
||||
@@ -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()
|
||||
|
||||
|
||||
@@ -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()
|
||||
|
||||
|
||||
@@ -1,34 +0,0 @@
|
||||
from fastvideo import VideoGenerator
|
||||
|
||||
PROMPT = (
|
||||
"A warm sunny backyard. The camera starts in a tight cinematic close-up "
|
||||
"of a woman and a man in their 30s, facing each other with serious "
|
||||
"expressions. The woman, emotional and dramatic, says softly, \"That's "
|
||||
"it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
|
||||
"annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
|
||||
"then mutters defensively, \"He's just having fun.\" The camera slowly "
|
||||
"pans right, revealing the grandfather in the garden wearing enormous "
|
||||
"butterfly wings, waving his arms in the air like he's trying to take "
|
||||
"off. He shouts, \"Wheeeew!\" as he flaps his wings with full commitment. "
|
||||
"The woman covers her face, on the verge of tears. The tone is deadpan, "
|
||||
"absurd, and quietly tragic."
|
||||
)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
"FastVideo/LTX2-Distilled-Diffusers",
|
||||
num_gpus=1,
|
||||
)
|
||||
|
||||
output_path = "outputs_video/ltx2_basic/output_ltx2_distilled_t2v.mp4"
|
||||
generator.generate_video(
|
||||
prompt=PROMPT,
|
||||
output_path=output_path,
|
||||
save_video=True,
|
||||
)
|
||||
generator.shutdown()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -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,
|
||||
|
||||
@@ -46,7 +46,7 @@ async 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,
|
||||
|
||||
@@ -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,
|
||||
)
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -15,14 +15,12 @@ def main() -> None:
|
||||
"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,
|
||||
# TurboDiffusion uses a custom pipeline with RCM scheduler
|
||||
override_pipeline_cls_name="TurboDiffusionPipeline",
|
||||
)
|
||||
|
||||
# Generate videos with the same simple API, regardless of GPU count
|
||||
# TurboDiffusion defaults: guidance_scale=1.0 and num_inference_steps=4 (from config)
|
||||
# TurboDiffusion uses guidance_scale=1.0 (no CFG) and only 4 steps
|
||||
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 "
|
||||
@@ -32,7 +30,9 @@ def main() -> None:
|
||||
prompt,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
num_inference_steps=4,
|
||||
seed=42,
|
||||
guidance_scale=1.0,
|
||||
)
|
||||
|
||||
# Generate another video with a different prompt, without reloading the model!
|
||||
@@ -47,7 +47,9 @@ def main() -> None:
|
||||
prompt2,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
num_inference_steps=4,
|
||||
seed=42,
|
||||
guidance_scale=1.0,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -15,6 +15,8 @@ def main() -> None:
|
||||
"loayrashid/TurboWan2.1-T2V-14B-Diffusers",
|
||||
# 14B model needs more GPUs
|
||||
num_gpus=2,
|
||||
# TurboDiffusion uses a custom pipeline with RCM scheduler
|
||||
override_pipeline_cls_name="TurboDiffusionPipeline",
|
||||
)
|
||||
|
||||
prompt = (
|
||||
@@ -26,7 +28,9 @@ def main() -> None:
|
||||
prompt,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
num_inference_steps=4,
|
||||
seed=42,
|
||||
guidance_scale=1.0,
|
||||
)
|
||||
|
||||
# Generate another video with a different prompt, without reloading the model!
|
||||
@@ -41,7 +45,9 @@ def main() -> None:
|
||||
prompt2,
|
||||
output_path=OUTPUT_PATH,
|
||||
save_video=True,
|
||||
num_inference_steps=4,
|
||||
seed=42,
|
||||
guidance_scale=1.0,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -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()
|
||||
@@ -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,
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -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()
|
||||
|
||||
@@ -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, ...)
|
||||
|
||||
@@ -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();
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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());
|
||||
}
|
||||
});
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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;
|
||||
|
||||
@@ -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"
|
||||
|
||||
@@ -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)
|
||||
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -1 +1 @@
|
||||
__version__ = "0.2.4"
|
||||
__version__ = "0.2.1"
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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"
|
||||
|
||||
|
||||
@@ -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)
|
||||
|
||||
|
||||
@@ -2,16 +2,5 @@ from fastvideo.configs.models.base import ModelConfig
|
||||
from fastvideo.configs.models.dits.base import DiTConfig
|
||||
from fastvideo.configs.models.encoders.base import EncoderConfig
|
||||
from fastvideo.configs.models.vaes.base import VAEConfig
|
||||
from fastvideo.configs.models.audio import (LTX2AudioDecoderConfig,
|
||||
LTX2AudioEncoderConfig,
|
||||
LTX2VocoderConfig)
|
||||
|
||||
__all__ = [
|
||||
"ModelConfig",
|
||||
"VAEConfig",
|
||||
"DiTConfig",
|
||||
"EncoderConfig",
|
||||
"LTX2AudioEncoderConfig",
|
||||
"LTX2AudioDecoderConfig",
|
||||
"LTX2VocoderConfig",
|
||||
]
|
||||
__all__ = ["ModelConfig", "VAEConfig", "DiTConfig", "EncoderConfig"]
|
||||
|
||||
@@ -1,13 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from fastvideo.configs.models.audio.ltx2_audio_vae import (
|
||||
LTX2AudioDecoderConfig,
|
||||
LTX2AudioEncoderConfig,
|
||||
LTX2VocoderConfig,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"LTX2AudioEncoderConfig",
|
||||
"LTX2AudioDecoderConfig",
|
||||
"LTX2VocoderConfig",
|
||||
]
|
||||
@@ -1,31 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
LTX-2 audio VAE and vocoder configuration.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from fastvideo.configs.models.base import ArchConfig, ModelConfig
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2AudioArchConfig(ArchConfig):
|
||||
architectures: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2AudioEncoderConfig(ModelConfig):
|
||||
arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
|
||||
architectures=["LTX2AudioEncoder"]))
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2AudioDecoderConfig(ModelConfig):
|
||||
arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
|
||||
architectures=["LTX2AudioDecoder"]))
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2VocoderConfig(ModelConfig):
|
||||
arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
|
||||
architectures=["LTX2Vocoder"]))
|
||||
@@ -3,12 +3,11 @@ from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
|
||||
from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
|
||||
from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
|
||||
from fastvideo.configs.models.dits.longcat import LongCatVideoConfig
|
||||
from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
|
||||
from fastvideo.configs.models.dits.stepvideo import StepVideoConfig
|
||||
from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
|
||||
|
||||
__all__ = [
|
||||
"HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
|
||||
"StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
|
||||
"LongCatVideoConfig", "LTX2VideoConfig"
|
||||
"LongCatVideoConfig"
|
||||
]
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -1,84 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
LTX-2 Transformer configuration for native FastVideo integration.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
|
||||
|
||||
|
||||
def is_ltx2_blocks(name: str, _module) -> bool:
|
||||
"""FSDP shard condition for LTX-2 transformer blocks."""
|
||||
return "transformer_blocks" in name
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2VideoArchConfig(DiTArchConfig):
|
||||
"""Architecture configuration for LTX-2 video transformer."""
|
||||
|
||||
_fsdp_shard_conditions: list = field(
|
||||
default_factory=lambda: [is_ltx2_blocks])
|
||||
_compile_conditions: list = field(default_factory=lambda: [is_ltx2_blocks])
|
||||
|
||||
# Parameter name mapping for weight conversion (hf/comfy -> FastVideo)
|
||||
param_names_mapping: dict = field(
|
||||
default_factory=lambda: {
|
||||
r"^model\.diffusion_model\.(.*)$": r"model.\1",
|
||||
r"^diffusion_model\.(.*)$": r"model.\1",
|
||||
r"^model\.(.*)$": r"model.\1",
|
||||
r"^(.*)$": r"model.\1",
|
||||
})
|
||||
|
||||
reverse_param_names_mapping: dict = field(default_factory=lambda: {})
|
||||
lora_param_names_mapping: dict = field(default_factory=lambda: {})
|
||||
|
||||
# Core transformer settings (defaults from LTX-2 metadata)
|
||||
num_attention_heads: int = 32
|
||||
attention_head_dim: int = 128
|
||||
num_layers: int = 48
|
||||
cross_attention_dim: int = 4096
|
||||
caption_channels: int = 3840
|
||||
norm_eps: float = 1e-6
|
||||
attention_type: str = "default"
|
||||
rope_type: str = "split"
|
||||
double_precision_rope: bool = True
|
||||
|
||||
positional_embedding_theta: float = 10000.0
|
||||
positional_embedding_max_pos: list[int] = field(
|
||||
default_factory=lambda: [20, 2048, 2048])
|
||||
timestep_scale_multiplier: int = 1000
|
||||
use_middle_indices_grid: bool = True
|
||||
|
||||
# Patchification (video-only path)
|
||||
patch_size: tuple[int, int, int] = (1, 1, 1)
|
||||
num_channels_latents: int = 128
|
||||
in_channels: int | None = None
|
||||
out_channels: int | None = None
|
||||
|
||||
# Audio defaults (reserved for joint AV ports)
|
||||
audio_num_attention_heads: int = 32
|
||||
audio_attention_head_dim: int = 64
|
||||
audio_in_channels: int = 128
|
||||
audio_out_channels: int = 128
|
||||
audio_cross_attention_dim: int = 2048
|
||||
audio_positional_embedding_max_pos: list[int] = field(
|
||||
default_factory=lambda: [20])
|
||||
av_ca_timestep_scale_multiplier: int = 1
|
||||
|
||||
def __post_init__(self):
|
||||
super().__post_init__()
|
||||
patch_volume = self.patch_size[0] * self.patch_size[
|
||||
1] * self.patch_size[2]
|
||||
if self.in_channels is None:
|
||||
self.in_channels = self.num_channels_latents * patch_volume
|
||||
if self.out_channels is None:
|
||||
self.out_channels = self.in_channels
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2VideoConfig(DiTConfig):
|
||||
"""Main configuration for LTX-2 transformer."""
|
||||
|
||||
arch_config: DiTArchConfig = field(default_factory=LTX2VideoArchConfig)
|
||||
prefix: str = "ltx2"
|
||||
@@ -7,12 +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
|
||||
from fastvideo.configs.models.encoders.gemma import LTX2GemmaConfig
|
||||
|
||||
__all__ = [
|
||||
"EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
|
||||
"BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig",
|
||||
"WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig",
|
||||
"Qwen2_5_VLConfig", "Reason1ArchConfig", "Reason1Config", "LTX2GemmaConfig"
|
||||
"Qwen2_5_VLConfig"
|
||||
]
|
||||
|
||||
@@ -1,48 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from fastvideo.configs.models.encoders.base import (
|
||||
TextEncoderArchConfig,
|
||||
TextEncoderConfig,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2GemmaArchConfig(TextEncoderArchConfig):
|
||||
architectures: list[str] = field(
|
||||
default_factory=lambda: ["LTX2GemmaTextEncoderModel"])
|
||||
hidden_size: int = 3840
|
||||
num_hidden_layers: int = 48
|
||||
num_attention_heads: int = 30
|
||||
text_len: int = 1024
|
||||
pad_token_id: int = 0
|
||||
eos_token_id: int = 2
|
||||
|
||||
gemma_model_path: str = ""
|
||||
gemma_dtype: str = "bfloat16"
|
||||
padding_side: str = "left"
|
||||
|
||||
feature_extractor_in_features: int = 3840 * 49
|
||||
feature_extractor_out_features: int = 3840
|
||||
|
||||
connector_num_attention_heads: int = 30
|
||||
connector_attention_head_dim: int = 128
|
||||
connector_num_layers: int = 2
|
||||
connector_positional_embedding_theta: float = 10000.0
|
||||
connector_positional_embedding_max_pos: list[int] = field(
|
||||
default_factory=lambda: [4096])
|
||||
connector_rope_type: str = "split"
|
||||
connector_double_precision_rope: bool = False
|
||||
connector_num_learnable_registers: int | None = 128
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
super().__post_init__()
|
||||
self.tokenizer_kwargs["padding"] = "max_length"
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2GemmaConfig(TextEncoderConfig):
|
||||
arch_config: TextEncoderArchConfig = field(
|
||||
default_factory=LTX2GemmaArchConfig)
|
||||
|
||||
prefix: str = "ltx2_gemma"
|
||||
@@ -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"
|
||||
@@ -1,8 +1,6 @@
|
||||
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.ltx2vae import LTX2VAEConfig
|
||||
from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
|
||||
from fastvideo.configs.models.vaes.wanvae import WanVAEConfig
|
||||
|
||||
@@ -11,7 +9,5 @@ __all__ = [
|
||||
"WanVAEConfig",
|
||||
"StepVideoVAEConfig",
|
||||
"CosmosVAEConfig",
|
||||
"Cosmos25VAEConfig",
|
||||
"Hunyuan15VAEConfig",
|
||||
"LTX2VAEConfig",
|
||||
]
|
||||
|
||||
@@ -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
|
||||
@@ -1,45 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
LTX-2 VAE configuration.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2VAEArchConfig(VAEArchConfig):
|
||||
# Mirrors LTX-2 safetensors metadata config under "vae"
|
||||
_class_name: str = "CausalVideoAutoencoder"
|
||||
dims: int = 3
|
||||
in_channels: int = 3
|
||||
out_channels: int = 3
|
||||
latent_channels: int = 128
|
||||
encoder_blocks: list = field(default_factory=list)
|
||||
decoder_blocks: list = field(default_factory=list)
|
||||
patch_size: int = 4
|
||||
norm_layer: str = "pixel_norm"
|
||||
latent_log_var: str = "uniform"
|
||||
encoder_spatial_padding_mode: str = "zeros"
|
||||
decoder_spatial_padding_mode: str = "reflect"
|
||||
causal_decoder: bool = False
|
||||
timestep_conditioning: bool = True
|
||||
use_quant_conv: bool = False
|
||||
scaling_factor: float = 1.0
|
||||
normalize_latent_channels: bool = False
|
||||
|
||||
# Match FastVideo naming for compression ratios (LTX-2 default)
|
||||
temporal_compression_ratio: int = 8
|
||||
spatial_compression_ratio: int = 32
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2VAEConfig(VAEConfig):
|
||||
arch_config: VAEArchConfig = field(default_factory=LTX2VAEArchConfig)
|
||||
|
||||
# LTX-2 tiling defaults (match ltx_core.video_vae.TilingConfig.default()).
|
||||
ltx2_spatial_tile_size_in_pixels: int = 512
|
||||
ltx2_spatial_tile_overlap_in_pixels: int = 64
|
||||
ltx2_temporal_tile_size_in_frames: int = 64
|
||||
ltx2_temporal_tile_overlap_in_frames: int = 24
|
||||
@@ -1,10 +1,8 @@
|
||||
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.ltx2 import LTX2T2VConfig
|
||||
from fastvideo.configs.pipelines.registry import (
|
||||
get_pipeline_config_cls_from_name)
|
||||
from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
|
||||
@@ -17,6 +15,5 @@ __all__ = [
|
||||
"Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig", "SlidingTileAttnConfig",
|
||||
"WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig",
|
||||
"WanI2V720PConfig", "StepVideoT2VConfig", "SelfForcingWanT2V480PConfig",
|
||||
"CosmosConfig", "Cosmos25Config", "LTX2T2VConfig",
|
||||
"get_pipeline_config_cls_from_name"
|
||||
"CosmosConfig", "get_pipeline_config_cls_from_name"
|
||||
]
|
||||
|
||||
@@ -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
|
||||
@@ -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()))
|
||||
|
||||
|
||||
@@ -1,50 +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, ModelConfig,
|
||||
LTX2AudioDecoderConfig, LTX2VocoderConfig,
|
||||
VAEConfig)
|
||||
from fastvideo.configs.models.dits import LTX2VideoConfig
|
||||
from fastvideo.configs.models.encoders import BaseEncoderOutput, LTX2GemmaConfig
|
||||
from fastvideo.configs.models.vaes import LTX2VAEConfig
|
||||
from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
|
||||
|
||||
|
||||
def ltx2_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
|
||||
return outputs.last_hidden_state
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2T2VConfig(PipelineConfig):
|
||||
"""Configuration for LTX-2 T2V pipeline."""
|
||||
|
||||
dit_config: DiTConfig = field(default_factory=LTX2VideoConfig)
|
||||
vae_config: VAEConfig = field(default_factory=LTX2VAEConfig)
|
||||
vae_tiling: bool = True
|
||||
vae_sp: bool = False
|
||||
|
||||
text_encoder_configs: tuple[EncoderConfig, ...] = field(
|
||||
default_factory=lambda: (LTX2GemmaConfig(), ))
|
||||
preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
|
||||
default_factory=lambda: (preprocess_text, ))
|
||||
postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
|
||||
...] = field(default_factory=lambda:
|
||||
(ltx2_postprocess_text, ))
|
||||
|
||||
dit_precision: str = "bf16"
|
||||
vae_precision: str = "bf16"
|
||||
text_encoder_precisions: tuple[str, ...] = field(
|
||||
default_factory=lambda: ("bf16", ))
|
||||
|
||||
audio_decoder_config: ModelConfig = field(
|
||||
default_factory=LTX2AudioDecoderConfig)
|
||||
vocoder_config: ModelConfig = field(default_factory=LTX2VocoderConfig)
|
||||
audio_decoder_precision: str = "bf16"
|
||||
vocoder_precision: str = "bf16"
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
self.vae_config.load_encoder = False
|
||||
self.vae_config.load_decoder = True
|
||||
@@ -6,15 +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.ltx2 import LTX2T2VConfig
|
||||
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 (
|
||||
@@ -57,32 +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,
|
||||
# LTX-2 models
|
||||
"Lightricks/LTX-2": LTX2T2VConfig,
|
||||
"converted/ltx2_diffusers": LTX2T2VConfig,
|
||||
# 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":
|
||||
@@ -100,23 +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(),
|
||||
"ltx2":
|
||||
lambda id: "ltx2" in id.lower() or "ltx-2" 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,
|
||||
@@ -127,9 +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,
|
||||
"ltx2": LTX2T2VConfig,
|
||||
"stepvideo": StepVideoT2VConfig
|
||||
# Other fallbacks by architecture
|
||||
}
|
||||
|
||||
|
||||
@@ -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
|
||||
@@ -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",
|
||||
|
||||
@@ -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
|
||||
@@ -1,20 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
from dataclasses import dataclass
|
||||
|
||||
from fastvideo.configs.sample.base import SamplingParam
|
||||
|
||||
|
||||
@dataclass
|
||||
class LTX2SamplingParam(SamplingParam):
|
||||
"""Default sampling parameters for LTX-2 distilled T2V.
|
||||
"""
|
||||
|
||||
seed: int = 10
|
||||
num_frames: int = 121
|
||||
height: int = 1024
|
||||
width: int = 1536
|
||||
fps: int = 24
|
||||
num_inference_steps: int = 8
|
||||
guidance_scale: float = 1.0
|
||||
# No default negative_prompt for distilled models
|
||||
negative_prompt: str = ""
|
||||
@@ -9,8 +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
|
||||
from fastvideo.configs.sample.ltx2 import LTX2SamplingParam
|
||||
|
||||
# isort: off
|
||||
from fastvideo.configs.sample.wan import (
|
||||
@@ -28,11 +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,
|
||||
@@ -86,27 +79,11 @@ 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,
|
||||
|
||||
# LTX-2 models
|
||||
"Lightricks/LTX-2": LTX2SamplingParam,
|
||||
"FastVideo/LTX2-Distilled-Diffusers": LTX2SamplingParam,
|
||||
|
||||
# Add other specific weight variants
|
||||
}
|
||||
|
||||
@@ -128,14 +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(),
|
||||
"ltx2":
|
||||
lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
|
||||
# Add other pipeline architecture detectors
|
||||
}
|
||||
|
||||
@@ -152,11 +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,
|
||||
"ltx2": LTX2SamplingParam,
|
||||
# Other fallbacks by architecture
|
||||
}
|
||||
|
||||
@@ -180,6 +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)
|
||||
|
||||
|
||||
@@ -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
|
||||
@@ -18,8 +18,6 @@ import numpy as np
|
||||
import torch
|
||||
import torchvision
|
||||
from einops import rearrange
|
||||
import shutil
|
||||
import tempfile
|
||||
|
||||
from fastvideo.configs.sample import SamplingParam
|
||||
from fastvideo.fastvideo_args import FastVideoArgs
|
||||
@@ -391,11 +389,6 @@ class VideoGenerator:
|
||||
if batch.save_video:
|
||||
imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
|
||||
logger.info("Saved video to %s", output_path)
|
||||
audio = output_batch.extra.get("audio")
|
||||
audio_sample_rate = output_batch.extra.get("audio_sample_rate")
|
||||
if (audio is not None and audio_sample_rate is not None and
|
||||
not self._mux_audio(output_path, audio, audio_sample_rate)):
|
||||
logger.warning("Audio mux failed; saved video without audio.")
|
||||
|
||||
if batch.return_frames:
|
||||
return frames
|
||||
@@ -403,7 +396,6 @@ class VideoGenerator:
|
||||
return {
|
||||
"samples": samples,
|
||||
"frames": frames,
|
||||
"audio": output_batch.extra.get("audio"),
|
||||
"prompts": prompt,
|
||||
"size": (target_height, target_width, batch.num_frames),
|
||||
"generation_time": gen_time,
|
||||
@@ -413,98 +405,6 @@ class VideoGenerator:
|
||||
"trajectory_decoded": output_batch.trajectory_decoded,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _mux_audio(
|
||||
video_path: str,
|
||||
audio: torch.Tensor | np.ndarray,
|
||||
sample_rate: int,
|
||||
) -> bool:
|
||||
"""Mux audio into video using PyAV."""
|
||||
try:
|
||||
import av
|
||||
except ImportError:
|
||||
logger.warning("PyAV not installed; cannot mux audio. "
|
||||
"Install with: pip install av")
|
||||
return False
|
||||
|
||||
if torch.is_tensor(audio):
|
||||
audio_np = audio.detach().cpu().float().numpy()
|
||||
else:
|
||||
audio_np = np.asarray(audio, dtype=np.float32)
|
||||
|
||||
if audio_np.ndim == 1:
|
||||
audio_np = audio_np[:, None]
|
||||
elif audio_np.ndim == 2:
|
||||
if audio_np.shape[0] <= 8 and audio_np.shape[1] > audio_np.shape[0]:
|
||||
audio_np = audio_np.T
|
||||
else:
|
||||
logger.warning("Unexpected audio shape %s; skipping mux.",
|
||||
audio_np.shape)
|
||||
return False
|
||||
|
||||
audio_np = np.clip(audio_np, -1.0, 1.0)
|
||||
audio_int16 = (audio_np * 32767.0).astype(np.int16)
|
||||
num_channels = audio_int16.shape[1]
|
||||
layout = "stereo" if num_channels == 2 else "mono"
|
||||
|
||||
try:
|
||||
import wave
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
out_path = os.path.join(tmpdir, "muxed.mp4")
|
||||
wav_path = os.path.join(tmpdir, "audio.wav")
|
||||
|
||||
# Write audio to WAV file
|
||||
with wave.open(wav_path, "wb") as wav_file:
|
||||
wav_file.setnchannels(num_channels)
|
||||
wav_file.setsampwidth(2)
|
||||
wav_file.setframerate(sample_rate)
|
||||
wav_file.writeframes(audio_int16.tobytes())
|
||||
|
||||
# Open input video and audio
|
||||
input_video = av.open(video_path)
|
||||
input_audio = av.open(wav_path)
|
||||
|
||||
# Create output with both streams
|
||||
output = av.open(out_path, mode="w")
|
||||
|
||||
# Add video stream (copy codec from input)
|
||||
in_video_stream = input_video.streams.video[0]
|
||||
out_video_stream = output.add_stream(
|
||||
codec_name=in_video_stream.codec_context.name,
|
||||
rate=in_video_stream.average_rate,
|
||||
)
|
||||
out_video_stream.width = in_video_stream.width
|
||||
out_video_stream.height = in_video_stream.height
|
||||
out_video_stream.pix_fmt = in_video_stream.pix_fmt
|
||||
|
||||
# Add audio stream (AAC)
|
||||
out_audio_stream = output.add_stream("aac", rate=sample_rate)
|
||||
out_audio_stream.layout = layout
|
||||
|
||||
# Remux video (decode and re-encode to be safe)
|
||||
for frame in input_video.decode(video=0):
|
||||
for packet in out_video_stream.encode(frame):
|
||||
output.mux(packet)
|
||||
for packet in out_video_stream.encode():
|
||||
output.mux(packet)
|
||||
|
||||
# Encode audio
|
||||
for frame in input_audio.decode(audio=0):
|
||||
frame.pts = None # Let encoder assign PTS
|
||||
for packet in out_audio_stream.encode(frame):
|
||||
output.mux(packet)
|
||||
for packet in out_audio_stream.encode():
|
||||
output.mux(packet)
|
||||
|
||||
input_video.close()
|
||||
input_audio.close()
|
||||
output.close()
|
||||
shutil.move(out_path, video_path)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.warning("Audio mux failed: %s", e)
|
||||
return False
|
||||
|
||||
def set_lora_adapter(self,
|
||||
lora_nickname: str,
|
||||
lora_path: str | None = None) -> None:
|
||||
|
||||
@@ -132,8 +132,8 @@ 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
|
||||
dit_layerwise_offload: bool = False
|
||||
text_encoder_cpu_offload: bool = True
|
||||
image_encoder_cpu_offload: bool = True
|
||||
vae_cpu_offload: bool = True
|
||||
@@ -166,14 +166,6 @@ class FastVideoArgs:
|
||||
# Prompt text file for batch processing
|
||||
prompt_txt: str | None = None
|
||||
|
||||
# LTX-2 VAE tiling overrides
|
||||
ltx2_vae_tiling: bool | None = None
|
||||
ltx2_vae_spatial_tile_size_in_pixels: int | None = None
|
||||
ltx2_vae_spatial_tile_overlap_in_pixels: int | None = None
|
||||
ltx2_vae_temporal_tile_size_in_frames: int | None = None
|
||||
ltx2_vae_temporal_tile_overlap_in_frames: int | None = None
|
||||
ltx2_initial_latent_path: str | None = None
|
||||
|
||||
# model paths for correct deallocation
|
||||
model_paths: dict[str, str] = field(default_factory=dict)
|
||||
model_loaded: dict[str, bool] = field(default_factory=lambda: {
|
||||
@@ -211,44 +203,8 @@ class FastVideoArgs:
|
||||
logger.error("Failed to load V-MoBA config from %s: %s",
|
||||
self.moba_config_path, e)
|
||||
raise
|
||||
self._apply_ltx2_vae_overrides()
|
||||
self.check_fastvideo_args()
|
||||
|
||||
def _apply_ltx2_vae_overrides(self) -> None:
|
||||
if self.pipeline_config is None:
|
||||
return
|
||||
vae_config = self.pipeline_config.vae_config
|
||||
has_any = any(value is not None for value in (
|
||||
self.ltx2_vae_spatial_tile_size_in_pixels,
|
||||
self.ltx2_vae_spatial_tile_overlap_in_pixels,
|
||||
self.ltx2_vae_temporal_tile_size_in_frames,
|
||||
self.ltx2_vae_temporal_tile_overlap_in_frames,
|
||||
))
|
||||
if self.ltx2_vae_tiling is not None and hasattr(self.pipeline_config,
|
||||
"vae_tiling"):
|
||||
self.pipeline_config.vae_tiling = self.ltx2_vae_tiling
|
||||
elif has_any and hasattr(self.pipeline_config, "vae_tiling"):
|
||||
self.pipeline_config.vae_tiling = True
|
||||
|
||||
if hasattr(vae_config, "ltx2_spatial_tile_size_in_pixels"
|
||||
) and self.ltx2_vae_spatial_tile_size_in_pixels is not None:
|
||||
vae_config.ltx2_spatial_tile_size_in_pixels = (
|
||||
self.ltx2_vae_spatial_tile_size_in_pixels)
|
||||
if hasattr(
|
||||
vae_config, "ltx2_spatial_tile_overlap_in_pixels"
|
||||
) and self.ltx2_vae_spatial_tile_overlap_in_pixels is not None:
|
||||
vae_config.ltx2_spatial_tile_overlap_in_pixels = (
|
||||
self.ltx2_vae_spatial_tile_overlap_in_pixels)
|
||||
if hasattr(vae_config, "ltx2_temporal_tile_size_in_frames"
|
||||
) and self.ltx2_vae_temporal_tile_size_in_frames is not None:
|
||||
vae_config.ltx2_temporal_tile_size_in_frames = (
|
||||
self.ltx2_vae_temporal_tile_size_in_frames)
|
||||
if hasattr(
|
||||
vae_config, "ltx2_temporal_tile_overlap_in_frames"
|
||||
) and self.ltx2_vae_temporal_tile_overlap_in_frames is not None:
|
||||
vae_config.ltx2_temporal_tile_overlap_in_frames = (
|
||||
self.ltx2_vae_temporal_tile_overlap_in_frames)
|
||||
|
||||
@staticmethod
|
||||
def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
|
||||
# Model and path configuration
|
||||
@@ -369,44 +325,6 @@ class FastVideoArgs:
|
||||
"Path to a text file containing prompts (one per line) for batch processing",
|
||||
)
|
||||
|
||||
# LTX-2 VAE tiling overrides
|
||||
parser.add_argument(
|
||||
"--ltx2-vae-tiling",
|
||||
action=StoreBoolean,
|
||||
default=FastVideoArgs.ltx2_vae_tiling,
|
||||
help="Enable LTX-2 VAE tiling overrides.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ltx2-vae-spatial-tile-size-in-pixels",
|
||||
type=int,
|
||||
default=FastVideoArgs.ltx2_vae_spatial_tile_size_in_pixels,
|
||||
help="LTX-2 VAE spatial tile size in pixels.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ltx2-vae-spatial-tile-overlap-in-pixels",
|
||||
type=int,
|
||||
default=FastVideoArgs.ltx2_vae_spatial_tile_overlap_in_pixels,
|
||||
help="LTX-2 VAE spatial tile overlap in pixels.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ltx2-vae-temporal-tile-size-in-frames",
|
||||
type=int,
|
||||
default=FastVideoArgs.ltx2_vae_temporal_tile_size_in_frames,
|
||||
help="LTX-2 VAE temporal tile size in frames.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ltx2-vae-temporal-tile-overlap-in-frames",
|
||||
type=int,
|
||||
default=FastVideoArgs.ltx2_vae_temporal_tile_overlap_in_frames,
|
||||
help="LTX-2 VAE temporal tile overlap in frames.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ltx2-initial-latent-path",
|
||||
type=str,
|
||||
default=FastVideoArgs.ltx2_initial_latent_path,
|
||||
help="Path to load/save a precomputed LTX-2 initial latent.",
|
||||
)
|
||||
|
||||
# LoRA parameters (inference-time adapter loading)
|
||||
parser.add_argument(
|
||||
"--lora-path",
|
||||
@@ -513,9 +431,7 @@ class FastVideoArgs:
|
||||
"--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",
|
||||
|
||||
@@ -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)
|
||||
@@ -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)]
|
||||
@@ -1,9 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from fastvideo.models.audio.ltx2_audio_vae import (
|
||||
LTX2AudioDecoder,
|
||||
LTX2AudioEncoder,
|
||||
LTX2Vocoder,
|
||||
)
|
||||
|
||||
__all__ = ["LTX2AudioEncoder", "LTX2AudioDecoder", "LTX2Vocoder"]
|
||||
File diff suppressed because it is too large
Load Diff
@@ -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:
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -734,8 +734,18 @@ class WanTransformer3DModel(CachableDiT):
|
||||
block, hidden_states, encoder_hidden_states,
|
||||
timestep_proj, freqs_cis, attention_mask)
|
||||
else:
|
||||
for block in self.blocks:
|
||||
hidden_states = block(hidden_states, encoder_hidden_states,
|
||||
offload_mgr = getattr(self, "_layerwise_offload_manager", None)
|
||||
use_offload = offload_mgr is not None and getattr(offload_mgr, "enabled", False)
|
||||
|
||||
for i, block in enumerate(self.blocks):
|
||||
scope = offload_mgr.layer_scope(
|
||||
prefetch_layer_idx=i + 1 if i + 1 < len(self.blocks) else None,
|
||||
release_layer_idx=i,
|
||||
non_blocking=True,
|
||||
) if use_offload else nullcontext()
|
||||
|
||||
with scope:
|
||||
hidden_states = block(hidden_states, encoder_hidden_states,
|
||||
timestep_proj, freqs_cis, attention_mask)
|
||||
# if teacache is enabled, we need to cache the original hidden states
|
||||
|
||||
|
||||
@@ -1,563 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
import os
|
||||
from typing import Iterable
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import Gemma3ForConditionalGeneration
|
||||
|
||||
from fastvideo.configs.models.encoders import BaseEncoderOutput, TextEncoderConfig
|
||||
from fastvideo.models.encoders.base import TextEncoder
|
||||
from fastvideo.models.dits.ltx2 import (
|
||||
FeedForward,
|
||||
LTXRopeType,
|
||||
apply_ltx_rotary_emb,
|
||||
generate_ltx_freq_grid_np,
|
||||
generate_ltx_freq_grid_pytorch,
|
||||
precompute_ltx_freqs_cis,
|
||||
)
|
||||
from fastvideo.models.loader.weight_utils import default_weight_loader
|
||||
from fastvideo.platforms import AttentionBackendEnum
|
||||
|
||||
|
||||
def _debug_log_line(message: str) -> None:
|
||||
if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") != "1":
|
||||
return
|
||||
log_path = os.getenv("LTX2_PIPELINE_DEBUG_PATH", "")
|
||||
if not log_path:
|
||||
return
|
||||
log_dir = os.path.dirname(log_path)
|
||||
if log_dir:
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
with open(log_path, "a", encoding="utf-8") as f:
|
||||
f.write(message + "\n")
|
||||
|
||||
|
||||
def _debug_gemma_log_line(message: str) -> None:
|
||||
log_path = os.getenv("LTX2_FASTVIDEO_GEMMA_LOG", "")
|
||||
if not log_path:
|
||||
return
|
||||
log_dir = os.path.dirname(log_path)
|
||||
if log_dir:
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
with open(log_path, "a", encoding="utf-8") as f:
|
||||
f.write(message + "\n")
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class GemmaConnectorConfig:
|
||||
num_attention_heads: int
|
||||
attention_head_dim: int
|
||||
num_layers: int
|
||||
positional_embedding_theta: float
|
||||
positional_embedding_max_pos: list[int]
|
||||
rope_type: LTXRopeType
|
||||
double_precision_rope: bool
|
||||
num_learnable_registers: int | None
|
||||
|
||||
|
||||
class GemmaFeaturesExtractorProjLinear(nn.Module):
|
||||
"""Linear projection that aggregates stacked Gemma hidden states."""
|
||||
|
||||
def __init__(self, in_features: int, out_features: int) -> None:
|
||||
super().__init__()
|
||||
self.aggregate_embed = nn.Linear(in_features, out_features, bias=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return self.aggregate_embed(x)
|
||||
|
||||
|
||||
class _BasicTransformerBlock1D(nn.Module):
|
||||
"""1D transformer block for connector processing."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
heads: int,
|
||||
dim_head: int,
|
||||
rope_type: LTXRopeType,
|
||||
norm_eps: float = 1e-6,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.attn1 = _GemmaAttention(
|
||||
query_dim=dim,
|
||||
context_dim=None,
|
||||
heads=heads,
|
||||
dim_head=dim_head,
|
||||
norm_eps=norm_eps,
|
||||
rope_type=rope_type,
|
||||
)
|
||||
self.ff = FeedForward(dim, dim_out=dim)
|
||||
self.norm_eps = norm_eps
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: torch.Tensor | None = None,
|
||||
pe: tuple[torch.Tensor, torch.Tensor] | None = None,
|
||||
) -> torch.Tensor:
|
||||
norm_hidden_states = torch.nn.functional.rms_norm(
|
||||
hidden_states, (hidden_states.shape[-1],), eps=self.norm_eps
|
||||
)
|
||||
if norm_hidden_states.ndim == 4:
|
||||
norm_hidden_states = norm_hidden_states.squeeze(1)
|
||||
|
||||
attn_output = self.attn1(
|
||||
norm_hidden_states,
|
||||
mask=attention_mask,
|
||||
pe=pe,
|
||||
)
|
||||
hidden_states = attn_output + hidden_states
|
||||
if hidden_states.ndim == 4:
|
||||
hidden_states = hidden_states.squeeze(1)
|
||||
|
||||
norm_hidden_states = torch.nn.functional.rms_norm(
|
||||
hidden_states, (hidden_states.shape[-1],), eps=self.norm_eps
|
||||
)
|
||||
ff_output = self.ff(norm_hidden_states)
|
||||
hidden_states = ff_output + hidden_states
|
||||
if hidden_states.ndim == 4:
|
||||
hidden_states = hidden_states.squeeze(1)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class _GemmaAttention(nn.Module):
|
||||
"""Attention implementation aligned with LTX-2 text encoder."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
query_dim: int,
|
||||
context_dim: int | None,
|
||||
heads: int,
|
||||
dim_head: int,
|
||||
norm_eps: float,
|
||||
rope_type: LTXRopeType,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = query_dim if context_dim is None else context_dim
|
||||
|
||||
self.heads = heads
|
||||
self.dim_head = dim_head
|
||||
self.rope_type = rope_type
|
||||
|
||||
self.q_norm = torch.nn.RMSNorm(inner_dim, eps=norm_eps)
|
||||
self.k_norm = torch.nn.RMSNorm(inner_dim, eps=norm_eps)
|
||||
self.to_q = nn.Linear(query_dim, inner_dim, bias=True)
|
||||
self.to_k = nn.Linear(context_dim, inner_dim, bias=True)
|
||||
self.to_v = nn.Linear(context_dim, inner_dim, bias=True)
|
||||
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim, bias=True), nn.Identity())
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
context: torch.Tensor | None = None,
|
||||
mask: torch.Tensor | None = None,
|
||||
pe: tuple[torch.Tensor, torch.Tensor] | None = None,
|
||||
k_pe: tuple[torch.Tensor, torch.Tensor] | None = None,
|
||||
) -> torch.Tensor:
|
||||
q = self.to_q(x)
|
||||
context = x if context is None else context
|
||||
k = self.to_k(context)
|
||||
v = self.to_v(context)
|
||||
|
||||
q = self.q_norm(q)
|
||||
k = self.k_norm(k)
|
||||
|
||||
if pe is not None:
|
||||
q = apply_ltx_rotary_emb(q, pe, self.rope_type)
|
||||
k = apply_ltx_rotary_emb(k, pe if k_pe is None else k_pe, self.rope_type)
|
||||
|
||||
b, q_len, _ = q.shape
|
||||
k_len = k.shape[1]
|
||||
q = q.view(b, q_len, self.heads, self.dim_head).transpose(1, 2)
|
||||
k = k.view(b, k_len, self.heads, self.dim_head).transpose(1, 2)
|
||||
v = v.view(b, k_len, self.heads, self.dim_head).transpose(1, 2)
|
||||
|
||||
if mask is not None:
|
||||
if mask.ndim == 2:
|
||||
mask = mask.unsqueeze(0)
|
||||
if mask.ndim == 3:
|
||||
mask = mask.unsqueeze(1)
|
||||
|
||||
out = torch.nn.functional.scaled_dot_product_attention(
|
||||
q,
|
||||
k,
|
||||
v,
|
||||
attn_mask=mask,
|
||||
dropout_p=0.0,
|
||||
is_causal=False,
|
||||
)
|
||||
out = out.transpose(1, 2).reshape(b, q_len, -1)
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
class Embeddings1DConnector(nn.Module):
|
||||
"""Transformer connector that refines Gemma embeddings for LTX-2."""
|
||||
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
def __init__(self, config: GemmaConnectorConfig) -> None:
|
||||
super().__init__()
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.inner_dim = config.num_attention_heads * config.attention_head_dim
|
||||
self.positional_embedding_theta = config.positional_embedding_theta
|
||||
self.positional_embedding_max_pos = config.positional_embedding_max_pos
|
||||
self.rope_type = config.rope_type
|
||||
self.double_precision_rope = config.double_precision_rope
|
||||
self.transformer_1d_blocks = nn.ModuleList(
|
||||
[
|
||||
_BasicTransformerBlock1D(
|
||||
dim=self.inner_dim,
|
||||
heads=config.num_attention_heads,
|
||||
dim_head=config.attention_head_dim,
|
||||
rope_type=config.rope_type,
|
||||
)
|
||||
for _ in range(config.num_layers)
|
||||
]
|
||||
)
|
||||
self.num_learnable_registers = config.num_learnable_registers
|
||||
if self.num_learnable_registers:
|
||||
self.learnable_registers = nn.Parameter(
|
||||
torch.rand(
|
||||
self.num_learnable_registers,
|
||||
self.inner_dim,
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
* 2.0
|
||||
- 1.0
|
||||
)
|
||||
|
||||
def _replace_padded_with_learnable_registers(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: torch.Tensor,
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
assert hidden_states.shape[1] % self.num_learnable_registers == 0, (
|
||||
f"Hidden states sequence length {hidden_states.shape[1]} must be divisible by "
|
||||
f"num_learnable_registers {self.num_learnable_registers}."
|
||||
)
|
||||
|
||||
num_registers_duplications = (
|
||||
hidden_states.shape[1] // self.num_learnable_registers
|
||||
)
|
||||
learnable_registers = torch.tile(
|
||||
self.learnable_registers, (num_registers_duplications, 1)
|
||||
)
|
||||
attention_mask_binary = (
|
||||
attention_mask.squeeze(1).squeeze(1).unsqueeze(-1) >= -9000.0
|
||||
).int()
|
||||
|
||||
non_zero_hidden_states = hidden_states[
|
||||
:, attention_mask_binary.squeeze().bool(), :
|
||||
]
|
||||
non_zero_nums = non_zero_hidden_states.shape[1]
|
||||
pad_length = hidden_states.shape[1] - non_zero_nums
|
||||
adjusted_hidden_states = torch.nn.functional.pad(
|
||||
non_zero_hidden_states, pad=(0, 0, 0, pad_length), value=0
|
||||
)
|
||||
flipped_mask = torch.flip(attention_mask_binary, dims=[1])
|
||||
hidden_states = flipped_mask * adjusted_hidden_states + (
|
||||
1 - flipped_mask
|
||||
) * learnable_registers
|
||||
|
||||
attention_mask = torch.full_like(
|
||||
attention_mask,
|
||||
0.0,
|
||||
dtype=attention_mask.dtype,
|
||||
device=attention_mask.device,
|
||||
)
|
||||
|
||||
return hidden_states, attention_mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: torch.Tensor | None = None,
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
if self.num_learnable_registers:
|
||||
hidden_states, attention_mask = (
|
||||
self._replace_padded_with_learnable_registers(
|
||||
hidden_states, attention_mask
|
||||
)
|
||||
)
|
||||
|
||||
indices_grid = torch.arange(
|
||||
hidden_states.shape[1],
|
||||
dtype=torch.float32,
|
||||
device=hidden_states.device,
|
||||
)
|
||||
indices_grid = indices_grid[None, None, :]
|
||||
freq_grid_generator = (
|
||||
generate_ltx_freq_grid_np
|
||||
if self.double_precision_rope
|
||||
else generate_ltx_freq_grid_pytorch
|
||||
)
|
||||
freqs_cis = precompute_ltx_freqs_cis(
|
||||
indices_grid=indices_grid,
|
||||
dim=self.inner_dim,
|
||||
out_dtype=hidden_states.dtype,
|
||||
theta=self.positional_embedding_theta,
|
||||
max_pos=self.positional_embedding_max_pos,
|
||||
num_attention_heads=self.num_attention_heads,
|
||||
rope_type=self.rope_type,
|
||||
freq_grid_generator=freq_grid_generator,
|
||||
)
|
||||
|
||||
for block in self.transformer_1d_blocks:
|
||||
hidden_states = block(
|
||||
hidden_states, attention_mask=attention_mask, pe=freqs_cis
|
||||
)
|
||||
|
||||
hidden_states = torch.nn.functional.rms_norm(
|
||||
hidden_states, (hidden_states.shape[-1],), eps=1e-6
|
||||
)
|
||||
|
||||
return hidden_states, attention_mask
|
||||
|
||||
|
||||
class LTX2GemmaTextEncoderModel(TextEncoder):
|
||||
_supported_attention_backends = (
|
||||
AttentionBackendEnum.FLASH_ATTN,
|
||||
AttentionBackendEnum.TORCH_SDPA,
|
||||
)
|
||||
|
||||
def __init__(self, config: TextEncoderConfig) -> None:
|
||||
super().__init__(config)
|
||||
arch = config.arch_config
|
||||
|
||||
self.feature_extractor_linear = GemmaFeaturesExtractorProjLinear(
|
||||
in_features=arch.feature_extractor_in_features,
|
||||
out_features=arch.feature_extractor_out_features,
|
||||
)
|
||||
|
||||
connector_config = GemmaConnectorConfig(
|
||||
num_attention_heads=arch.connector_num_attention_heads,
|
||||
attention_head_dim=arch.connector_attention_head_dim,
|
||||
num_layers=arch.connector_num_layers,
|
||||
positional_embedding_theta=arch.connector_positional_embedding_theta,
|
||||
positional_embedding_max_pos=arch.connector_positional_embedding_max_pos,
|
||||
rope_type=LTXRopeType(arch.connector_rope_type),
|
||||
double_precision_rope=arch.connector_double_precision_rope,
|
||||
num_learnable_registers=arch.connector_num_learnable_registers,
|
||||
)
|
||||
self.embeddings_connector = Embeddings1DConnector(connector_config)
|
||||
self.audio_embeddings_connector = Embeddings1DConnector(connector_config)
|
||||
|
||||
self.gemma_model_path = arch.gemma_model_path
|
||||
self.gemma_dtype = arch.gemma_dtype
|
||||
self.padding_side = arch.padding_side
|
||||
self._gemma_model: Gemma3ForConditionalGeneration | None = None
|
||||
|
||||
def named_parameters(self, prefix: str = "", recurse: bool = True):
|
||||
for name, param in super().named_parameters(
|
||||
prefix=prefix, recurse=recurse
|
||||
):
|
||||
if name.startswith("gemma_model."):
|
||||
continue
|
||||
yield name, param
|
||||
|
||||
@property
|
||||
def gemma_model(self) -> Gemma3ForConditionalGeneration:
|
||||
if self._gemma_model is None:
|
||||
gemma_path = self.gemma_model_path
|
||||
if not gemma_path:
|
||||
raise ValueError(
|
||||
"gemma_model_path must be set (expected text_encoder/gemma)."
|
||||
)
|
||||
dtype = getattr(torch, self.gemma_dtype, torch.bfloat16)
|
||||
self._gemma_model = Gemma3ForConditionalGeneration.from_pretrained(
|
||||
gemma_path,
|
||||
local_files_only=True,
|
||||
torch_dtype=dtype,
|
||||
)
|
||||
# Configure model-level attention implementation when using TORCH_SDPA.
|
||||
# Note: torch.backends.cuda.enable_*_sdp() settings should be configured
|
||||
# at application/pipeline initialization level, not here, to avoid
|
||||
# unexpected side effects across the application.
|
||||
if os.getenv("FASTVIDEO_ATTENTION_BACKEND") == "TORCH_SDPA":
|
||||
if hasattr(self._gemma_model.config, "attn_implementation"):
|
||||
self._gemma_model.config.attn_implementation = "sdpa"
|
||||
if hasattr(self._gemma_model.config, "_attn_implementation"):
|
||||
self._gemma_model.config._attn_implementation = "sdpa"
|
||||
device = next(self.feature_extractor_linear.parameters()).device
|
||||
self._gemma_model.to(device=device)
|
||||
self._gemma_model.eval()
|
||||
return self._gemma_model
|
||||
|
||||
def _run_feature_extractor(
|
||||
self,
|
||||
hidden_states: tuple[torch.Tensor, ...],
|
||||
attention_mask: torch.Tensor,
|
||||
padding_side: str,
|
||||
) -> torch.Tensor:
|
||||
encoded_text_features = torch.stack(hidden_states, dim=-1)
|
||||
if os.getenv("LTX2_FASTVIDEO_GEMMA_LOG", ""):
|
||||
for idx, layer in enumerate(hidden_states):
|
||||
_debug_gemma_log_line(
|
||||
f"fastvideo:gemma_hidden_state_{idx}"
|
||||
f":sum={layer.float().sum().item():.6f}"
|
||||
)
|
||||
_debug_gemma_log_line(
|
||||
"fastvideo:gemma_hidden_states_stack"
|
||||
f":sum={encoded_text_features.float().sum().item():.6f}"
|
||||
)
|
||||
encoded_text_features_dtype = encoded_text_features.dtype
|
||||
sequence_lengths = attention_mask.sum(dim=-1)
|
||||
normed_text_features = _norm_and_concat_padded_batch(
|
||||
encoded_text_features, sequence_lengths, padding_side=padding_side
|
||||
)
|
||||
return self.feature_extractor_linear(
|
||||
normed_text_features.to(encoded_text_features_dtype)
|
||||
)
|
||||
|
||||
def _convert_to_additive_mask(
|
||||
self, attention_mask: torch.Tensor, dtype: torch.dtype
|
||||
) -> torch.Tensor:
|
||||
return (attention_mask - 1).to(dtype).reshape(
|
||||
(attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
|
||||
) * torch.finfo(dtype).max
|
||||
|
||||
def _run_connectors(
|
||||
self,
|
||||
encoded_input: torch.Tensor,
|
||||
attention_mask: torch.Tensor,
|
||||
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
connector_attention_mask = self._convert_to_additive_mask(
|
||||
attention_mask, encoded_input.dtype
|
||||
)
|
||||
encoded, encoded_connector_attention_mask = self.embeddings_connector(
|
||||
encoded_input, connector_attention_mask
|
||||
)
|
||||
|
||||
attention_mask = (encoded_connector_attention_mask < 0.000001).to(
|
||||
torch.int64
|
||||
)
|
||||
attention_mask = attention_mask.reshape(
|
||||
[encoded.shape[0], encoded.shape[1], 1]
|
||||
)
|
||||
encoded = encoded * attention_mask
|
||||
|
||||
encoded_for_audio, _ = self.audio_embeddings_connector(
|
||||
encoded_input, connector_attention_mask
|
||||
)
|
||||
|
||||
return encoded, encoded_for_audio, attention_mask.squeeze(-1)
|
||||
|
||||
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:
|
||||
if input_ids is None:
|
||||
raise ValueError("input_ids is required for Gemma text encoding.")
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones_like(input_ids)
|
||||
|
||||
model = self.gemma_model
|
||||
input_ids = input_ids.to(device=model.device)
|
||||
attention_mask = attention_mask.to(device=model.device)
|
||||
outputs = model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
output_hidden_states=True,
|
||||
return_dict=True,
|
||||
)
|
||||
|
||||
encoded_inputs = self._run_feature_extractor(
|
||||
outputs.hidden_states,
|
||||
attention_mask,
|
||||
padding_side=self.padding_side,
|
||||
)
|
||||
if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") == "1":
|
||||
_debug_log_line(
|
||||
"fastvideo:gemma_feature"
|
||||
f":sum={encoded_inputs.float().sum().item():.6f} "
|
||||
f"shape={tuple(encoded_inputs.shape)}"
|
||||
)
|
||||
video_encoding, audio_encoding, attention_mask = self._run_connectors(
|
||||
encoded_inputs, attention_mask
|
||||
)
|
||||
if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") == "1":
|
||||
_debug_log_line(
|
||||
"fastvideo:gemma_video_encoding"
|
||||
f":sum={video_encoding.float().sum().item():.6f} "
|
||||
f"shape={tuple(video_encoding.shape)}"
|
||||
)
|
||||
_debug_log_line(
|
||||
"fastvideo:gemma_audio_encoding"
|
||||
f":sum={audio_encoding.float().sum().item():.6f} "
|
||||
f"shape={tuple(audio_encoding.shape)}"
|
||||
)
|
||||
|
||||
hidden_states = (audio_encoding, ) if output_hidden_states else None
|
||||
return BaseEncoderOutput(
|
||||
last_hidden_state=video_encoding,
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
)
|
||||
|
||||
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:
|
||||
if name == "aggregate_embed.weight":
|
||||
name = "feature_extractor_linear.aggregate_embed.weight"
|
||||
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)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
def _norm_and_concat_padded_batch(
|
||||
encoded_text: torch.Tensor,
|
||||
sequence_lengths: torch.Tensor,
|
||||
padding_side: str = "right",
|
||||
) -> torch.Tensor:
|
||||
b, t, d, l = encoded_text.shape
|
||||
device = encoded_text.device
|
||||
|
||||
token_indices = torch.arange(t, device=device)[None, :]
|
||||
if padding_side == "right":
|
||||
mask = token_indices < sequence_lengths[:, None]
|
||||
elif padding_side == "left":
|
||||
start_indices = t - sequence_lengths[:, None]
|
||||
mask = token_indices >= start_indices
|
||||
else:
|
||||
raise ValueError(
|
||||
f"padding_side must be 'left' or 'right', got {padding_side}"
|
||||
)
|
||||
|
||||
mask = mask.reshape(b, t, 1, 1)
|
||||
eps = 1e-6
|
||||
|
||||
masked = encoded_text.masked_fill(~mask, 0.0)
|
||||
denom = (sequence_lengths * d).view(b, 1, 1, 1)
|
||||
mean = masked.sum(dim=(1, 2), keepdim=True) / (denom + eps)
|
||||
|
||||
x_min = encoded_text.masked_fill(~mask, float("inf")).amin(
|
||||
dim=(1, 2), keepdim=True
|
||||
)
|
||||
x_max = encoded_text.masked_fill(~mask, float("-inf")).amax(
|
||||
dim=(1, 2), keepdim=True
|
||||
)
|
||||
range_ = x_max - x_min
|
||||
|
||||
normed = 8 * (encoded_text - mean) / (range_ + eps)
|
||||
normed = normed.reshape(b, t, -1)
|
||||
|
||||
mask_flattened = mask.reshape(b, t, 1).expand(-1, -1, d * l)
|
||||
normed = normed.masked_fill(~mask_flattened, 0.0)
|
||||
return normed
|
||||
File diff suppressed because it is too large
Load Diff
@@ -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
|
||||
)
|
||||
|
||||
@@ -15,7 +15,8 @@ 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, AutoTokenizer
|
||||
from transformers import UMT5EncoderModel
|
||||
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
|
||||
|
||||
from fastvideo.configs.models import EncoderConfig
|
||||
@@ -34,8 +35,8 @@ from fastvideo.models.loader.weight_utils import (
|
||||
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
|
||||
from fastvideo.models.layerwise_offload import LayerwiseOffloadManager
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
@@ -80,9 +81,6 @@ class ComponentLoader(ABC):
|
||||
"transformer": (TransformerLoader, "diffusers"),
|
||||
"transformer_2": (TransformerLoader, "diffusers"),
|
||||
"vae": (VAELoader, "diffusers"),
|
||||
"audio_vae": (AudioDecoderLoader, "diffusers"),
|
||||
"audio_decoder": (AudioDecoderLoader, "diffusers"),
|
||||
"vocoder": (VocoderLoader, "diffusers"),
|
||||
"text_encoder": (TextEncoderLoader, "transformers"),
|
||||
"text_encoder_2": (TextEncoderLoader, "transformers"),
|
||||
"tokenizer": (TokenizerLoader, "transformers"),
|
||||
@@ -93,12 +91,10 @@ 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
|
||||
@@ -245,47 +241,6 @@ class TextEncoderLoader(ComponentLoader):
|
||||
model_config.pop("model_type", None)
|
||||
model_config.pop("tokenizer_class", None)
|
||||
model_config.pop("torch_dtype", None)
|
||||
repo_root = os.path.dirname(model_path)
|
||||
index_path = os.path.join(repo_root, "model_index.json")
|
||||
gemma_path = ""
|
||||
gemma_path_from_candidate = False
|
||||
if os.path.isfile(index_path):
|
||||
try:
|
||||
with open(index_path, encoding="utf-8") as f:
|
||||
model_index = json.load(f)
|
||||
gemma_path = model_index.get("gemma_model_path", "")
|
||||
except json.JSONDecodeError:
|
||||
gemma_path = ""
|
||||
if not gemma_path:
|
||||
candidate = os.path.normpath(os.path.join(model_path, "gemma"))
|
||||
if os.path.isdir(candidate):
|
||||
gemma_path = candidate
|
||||
gemma_path_from_candidate = True
|
||||
model_config["gemma_model_path"] = gemma_path
|
||||
if gemma_path and not gemma_path_from_candidate:
|
||||
if not os.path.isabs(gemma_path):
|
||||
model_config["gemma_model_path"] = os.path.normpath(
|
||||
os.path.join(repo_root, gemma_path)
|
||||
)
|
||||
transformer_config_path = os.path.join(
|
||||
repo_root, "transformer", "config.json"
|
||||
)
|
||||
if os.path.isfile(transformer_config_path):
|
||||
try:
|
||||
with open(transformer_config_path, encoding="utf-8") as f:
|
||||
transformer_config = json.load(f)
|
||||
if (
|
||||
"connector_double_precision_rope" not in model_config
|
||||
or not model_config["connector_double_precision_rope"]
|
||||
):
|
||||
if transformer_config.get("double_precision_rope") is True:
|
||||
model_config["connector_double_precision_rope"] = True
|
||||
if "connector_rope_type" not in model_config:
|
||||
rope_type = transformer_config.get("rope_type")
|
||||
if rope_type is not None:
|
||||
model_config["connector_rope_type"] = rope_type
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
logger.info("HF Model config: %s", model_config)
|
||||
|
||||
# @TODO(Wei): Better way to handle this?
|
||||
@@ -324,7 +279,7 @@ class TextEncoderLoader(ComponentLoader):
|
||||
target_device: torch.device,
|
||||
fastvideo_args: FastVideoArgs,
|
||||
dtype: str = "fp16",
|
||||
use_text_encoder_override: bool = False, # prevent subclasses from misusing
|
||||
use_text_encoder_override: bool = False, # prevent subclasses from misusing
|
||||
):
|
||||
use_cpu_offload = (
|
||||
fastvideo_args.text_encoder_cpu_offload
|
||||
@@ -341,10 +296,7 @@ class TextEncoderLoader(ComponentLoader):
|
||||
)
|
||||
|
||||
# Set quantization config if specified
|
||||
if (
|
||||
use_text_encoder_override
|
||||
and fastvideo_args.override_text_encoder_quant is not None
|
||||
):
|
||||
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"
|
||||
@@ -361,10 +313,7 @@ class TextEncoderLoader(ComponentLoader):
|
||||
model: TextEncoder = model_cls(model_config) # type: ignore
|
||||
|
||||
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
|
||||
):
|
||||
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],
|
||||
@@ -391,7 +340,6 @@ 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(
|
||||
@@ -409,7 +357,7 @@ 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(
|
||||
@@ -423,7 +371,7 @@ 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,
|
||||
)
|
||||
# We only enable strict check for non-quantized models
|
||||
# that have loaded weights tracking currently.
|
||||
@@ -501,52 +449,13 @@ 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_side = None
|
||||
if hasattr(fastvideo_args.pipeline_config, "text_encoder_configs"):
|
||||
try:
|
||||
arch_config = fastvideo_args.pipeline_config.text_encoder_configs[
|
||||
0
|
||||
].arch_config
|
||||
padding_side = getattr(arch_config, "padding_side", None)
|
||||
except Exception:
|
||||
padding_side = None
|
||||
if padding_side:
|
||||
tokenizer.padding_side = padding_side
|
||||
if tokenizer.pad_token is None and tokenizer.eos_token is not None:
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
logger.info("Loaded tokenizer: %s", tokenizer.__class__.__name__)
|
||||
return tokenizer
|
||||
|
||||
@@ -557,12 +466,15 @@ class VAELoader(ComponentLoader):
|
||||
def load(self, model_path: str, fastvideo_args: FastVideoArgs):
|
||||
"""Load the VAE based on the model path, and inference args."""
|
||||
config = get_diffusers_config(model=model_path)
|
||||
class_name = config.get("_class_name")
|
||||
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."
|
||||
)
|
||||
fastvideo_args.model_paths["vae"] = model_path
|
||||
|
||||
vae_config = fastvideo_args.pipeline_config.vae_config
|
||||
vae_config.update_model_arch(config)
|
||||
|
||||
from fastvideo.platforms import current_platform
|
||||
|
||||
if fastvideo_args.vae_cpu_offload:
|
||||
@@ -579,149 +491,23 @@ class VAELoader(ComponentLoader):
|
||||
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()
|
||||
|
||||
# LTX-2 uses CausalVideoAutoencoder with nested "vae" config
|
||||
if class_name == "CausalVideoAutoencoder" and "vae" in config:
|
||||
vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
|
||||
vae = vae_cls(config).to(target_device)
|
||||
if hasattr(vae, "set_tiling_config"):
|
||||
vae_config = fastvideo_args.pipeline_config.vae_config
|
||||
vae.set_tiling_config(
|
||||
spatial_tile_size_in_pixels=getattr(
|
||||
vae_config, "ltx2_spatial_tile_size_in_pixels", 512),
|
||||
spatial_tile_overlap_in_pixels=getattr(
|
||||
vae_config, "ltx2_spatial_tile_overlap_in_pixels", 64),
|
||||
temporal_tile_size_in_frames=getattr(
|
||||
vae_config, "ltx2_temporal_tile_size_in_frames", 64),
|
||||
temporal_tile_overlap_in_frames=getattr(
|
||||
vae_config,
|
||||
"ltx2_temporal_tile_overlap_in_frames", 24),
|
||||
)
|
||||
else:
|
||||
config.pop("_class_name", None)
|
||||
vae_config = fastvideo_args.pipeline_config.vae_config
|
||||
vae_config.update_model_arch(config)
|
||||
vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
|
||||
vae = vae_cls(vae_config).to(target_device)
|
||||
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.
|
||||
os.path.join(str(model_path), "*.safetensors")
|
||||
)
|
||||
loaded = {}
|
||||
for sf_file in safetensors_list:
|
||||
loaded.update(safetensors_load_file(sf_file))
|
||||
|
||||
# LTX-2 CausalVideoAutoencoder needs per_channel_statistics remapping
|
||||
if class_name == "CausalVideoAutoencoder" and "vae" in config:
|
||||
per_channel_prefixes = (
|
||||
"per_channel_statistics.",
|
||||
"vae.per_channel_statistics.",
|
||||
)
|
||||
remapped = {}
|
||||
for key, tensor in loaded.items():
|
||||
remapped[key] = tensor
|
||||
for prefix in per_channel_prefixes:
|
||||
if key.startswith(prefix):
|
||||
suffix = key[len(prefix):]
|
||||
remapped.setdefault(
|
||||
f"encoder.per_channel_statistics.{suffix}",
|
||||
tensor,
|
||||
)
|
||||
remapped.setdefault(
|
||||
f"decoder.per_channel_statistics.{suffix}",
|
||||
tensor,
|
||||
)
|
||||
break
|
||||
loaded = remapped
|
||||
|
||||
vae.load_state_dict(loaded, strict=False)
|
||||
vae.load_state_dict(
|
||||
loaded, strict=False
|
||||
) # We might only load encoder or decoder
|
||||
|
||||
return vae.eval()
|
||||
|
||||
|
||||
class AudioDecoderLoader(ComponentLoader):
|
||||
"""Loader for LTX-2 audio decoder (audio_vae component)."""
|
||||
|
||||
def load(self, model_path: str, fastvideo_args: FastVideoArgs):
|
||||
config = get_diffusers_config(model=model_path)
|
||||
class_name = config.pop("_class_name", None) or "LTX2AudioDecoder"
|
||||
|
||||
model_cls, _ = ModelRegistry.resolve_model_cls(class_name)
|
||||
target_device = get_local_torch_device()
|
||||
|
||||
precision = getattr(
|
||||
fastvideo_args.pipeline_config, "audio_decoder_precision", "bf16"
|
||||
)
|
||||
with set_default_torch_dtype(PRECISION_TO_TYPE[precision]):
|
||||
audio_decoder = model_cls(config).to(target_device)
|
||||
|
||||
safetensors_list = glob.glob(
|
||||
os.path.join(str(model_path), "*.safetensors")
|
||||
)
|
||||
loaded: dict[str, torch.Tensor] = {}
|
||||
for sf_file in safetensors_list:
|
||||
loaded.update(safetensors_load_file(sf_file))
|
||||
|
||||
decoder_state = {}
|
||||
for name, tensor in loaded.items():
|
||||
if name.startswith("decoder."):
|
||||
decoder_state[name.replace("decoder.", "")] = tensor
|
||||
elif name.startswith("per_channel_statistics."):
|
||||
decoder_state[name] = tensor
|
||||
|
||||
target_module = getattr(audio_decoder, "model", audio_decoder)
|
||||
target_module.load_state_dict(decoder_state, strict=False)
|
||||
return audio_decoder.eval()
|
||||
|
||||
|
||||
class VocoderLoader(ComponentLoader):
|
||||
"""Loader for LTX-2 vocoder."""
|
||||
|
||||
def load(self, model_path: str, fastvideo_args: FastVideoArgs):
|
||||
config = get_diffusers_config(model=model_path)
|
||||
class_name = config.pop("_class_name", None) or "LTX2Vocoder"
|
||||
|
||||
model_cls, _ = ModelRegistry.resolve_model_cls(class_name)
|
||||
target_device = get_local_torch_device()
|
||||
|
||||
precision = getattr(
|
||||
fastvideo_args.pipeline_config, "vocoder_precision", "bf16"
|
||||
)
|
||||
with set_default_torch_dtype(PRECISION_TO_TYPE[precision]):
|
||||
vocoder = model_cls(config).to(target_device)
|
||||
|
||||
safetensors_list = glob.glob(
|
||||
os.path.join(str(model_path), "*.safetensors")
|
||||
)
|
||||
loaded: dict[str, torch.Tensor] = {}
|
||||
for sf_file in safetensors_list:
|
||||
loaded.update(safetensors_load_file(sf_file))
|
||||
|
||||
target_module = getattr(vocoder, "model", vocoder)
|
||||
target_module.load_state_dict(loaded, strict=False)
|
||||
return vocoder.eval()
|
||||
|
||||
|
||||
class TransformerLoader(ComponentLoader):
|
||||
"""Loader for transformer."""
|
||||
|
||||
@@ -795,17 +581,7 @@ class TransformerLoader(ComponentLoader):
|
||||
]
|
||||
|
||||
# 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},
|
||||
@@ -813,7 +589,6 @@ class TransformerLoader(ComponentLoader):
|
||||
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,
|
||||
@@ -836,19 +611,35 @@ class TransformerLoader(ComponentLoader):
|
||||
|
||||
model = model.eval()
|
||||
|
||||
if fastvideo_args.inference_mode and fastvideo_args.dit_layerwise_offload:
|
||||
# Check if model has nn.ModuleList for layerwise offload compatibility
|
||||
has_module_list = any(
|
||||
isinstance(m, nn.ModuleList) for m in model.children()
|
||||
)
|
||||
if has_module_list:
|
||||
enable_layerwise_offload(model)
|
||||
else:
|
||||
if fastvideo_args.dit_layerwise_offload and hasattr(model, "blocks"):
|
||||
# Check if this is a Wan model (only Wan models support layerwise offload)
|
||||
is_wan_model = "Wan" in cls_name
|
||||
if not is_wan_model:
|
||||
logger.warning(
|
||||
"Layerwise offload requested but model %s does not have "
|
||||
"nn.ModuleList structure. Skipping layerwise offload.",
|
||||
"Layerwise offload is currently only supported for Wan models. "
|
||||
"Model class '%s' does not support layerwise offload. "
|
||||
"Disabling layerwise offload for this model.",
|
||||
cls_name
|
||||
)
|
||||
else:
|
||||
try:
|
||||
num_layers = len(getattr(model, "blocks"))
|
||||
except TypeError:
|
||||
num_layers = None
|
||||
if isinstance(num_layers, int) and num_layers > 0:
|
||||
# Ensure model is on the correct device (CUDA) before initializing manager
|
||||
# This ensures non-managed parameters (embeddings, final norms) are on GPU
|
||||
model = model.to(get_local_torch_device())
|
||||
mgr = LayerwiseOffloadManager(
|
||||
model,
|
||||
module_list_attr="blocks",
|
||||
num_layers=num_layers,
|
||||
enabled=True,
|
||||
pin_cpu_memory=fastvideo_args.pin_cpu_memory,
|
||||
auto_initialize=True,
|
||||
)
|
||||
setattr(model, "_layerwise_offload_manager", mgr)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
|
||||
@@ -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,
|
||||
)
|
||||
@@ -295,26 +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."
|
||||
)
|
||||
|
||||
@@ -30,10 +30,8 @@ _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)
|
||||
"LTX2Transformer3DModel": ("dits", "ltx2", "LTX2Transformer3DModel"),
|
||||
}
|
||||
|
||||
_IMAGE_TO_VIDEO_DIT_MODELS = {
|
||||
@@ -52,10 +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"),
|
||||
"LTX2GemmaTextEncoderModel": ("encoders", "gemma", "LTX2GemmaTextEncoderModel"),
|
||||
}
|
||||
|
||||
_IMAGE_ENCODER_MODELS: dict[str, tuple] = {
|
||||
@@ -69,14 +63,7 @@ _VAE_MODELS = {
|
||||
("vaes", "hunyuanvae", "AutoencoderKLHunyuanVideo"),
|
||||
"AutoencoderKLHunyuanVideo15": ("vaes", "hunyuan15vae", "AutoencoderKLHunyuanVideo15"),
|
||||
"AutoencoderKLWan": ("vaes", "wanvae", "AutoencoderKLWan"),
|
||||
"AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo"),
|
||||
"CausalVideoAutoencoder": ("vaes", "ltx2vae", "LTX2CausalVideoAutoencoder"),
|
||||
}
|
||||
|
||||
_AUDIO_MODELS = {
|
||||
"LTX2AudioEncoder": ("audio", "ltx2_audio_vae", "LTX2AudioEncoder"),
|
||||
"LTX2AudioDecoder": ("audio", "ltx2_audio_vae", "LTX2AudioDecoder"),
|
||||
"LTX2Vocoder": ("audio", "ltx2_audio_vae", "LTX2Vocoder"),
|
||||
"AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo")
|
||||
}
|
||||
|
||||
_SCHEDULERS = {
|
||||
@@ -85,8 +72,6 @@ _SCHEDULERS = {
|
||||
"FlowMatchEulerDiscreteScheduler"),
|
||||
"UniPCMultistepScheduler":
|
||||
("schedulers", "scheduling_unipc_multistep", "UniPCMultistepScheduler"),
|
||||
"FlowUniPCMultistepScheduler":
|
||||
("schedulers", "scheduling_flow_unipc_multistep", "FlowUniPCMultistepScheduler"),
|
||||
"SelfForcingFlowMatchScheduler":
|
||||
("schedulers", "scheduling_self_forcing_flow_match",
|
||||
"SelfForcingFlowMatchScheduler"),
|
||||
@@ -100,7 +85,6 @@ _FAST_VIDEO_MODELS = {
|
||||
**_TEXT_ENCODER_MODELS,
|
||||
**_IMAGE_ENCODER_MODELS,
|
||||
**_VAE_MODELS,
|
||||
**_AUDIO_MODELS,
|
||||
**_SCHEDULERS,
|
||||
}
|
||||
|
||||
|
||||
@@ -109,9 +109,6 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
|
||||
sigmas = 1.0 - alphas
|
||||
sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32)
|
||||
|
||||
# Needed when final_sigmas_type == "sigma_min" (kept for compatibility).
|
||||
self.alphas_cumprod = torch.from_numpy(alphas).to(dtype=torch.float32)
|
||||
|
||||
if not use_dynamic_shifting:
|
||||
# when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
|
||||
assert shift is not None
|
||||
@@ -174,8 +171,6 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
|
||||
sigmas: list[float] | None = None,
|
||||
mu: float | None | None = None,
|
||||
shift: float | None | None = None,
|
||||
use_karras_sigmas: bool | None = None,
|
||||
use_kerras_sigma: bool | None = None,
|
||||
):
|
||||
"""
|
||||
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
|
||||
@@ -191,44 +186,21 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
|
||||
" you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`"
|
||||
)
|
||||
|
||||
|
||||
# Cosmos official uses `use_kerras_sigma=True` and a specific EDM sigma schedule.
|
||||
# Some external code uses the misspelling `use_kerras_sigma`; support both.
|
||||
if use_karras_sigmas is None and use_kerras_sigma is not None:
|
||||
use_karras_sigmas = use_kerras_sigma
|
||||
|
||||
if use_karras_sigmas:
|
||||
# Force to use the exact sigma used in official EDM sampler:
|
||||
# sigma_max=200, sigma_min=0.01, rho=7
|
||||
sigma_max = 200.0
|
||||
sigma_min = 0.01
|
||||
rho = 7.0
|
||||
# Match the official Cosmos implementation: Karras/EDM schedule with
|
||||
# `num_inference_steps + 1` points, then `final_sigmas_type="zero"`
|
||||
# appends the terminal sigma (0.0).
|
||||
ramp = np.arange(num_inference_steps + 1,
|
||||
dtype=np.float32) / float(num_inference_steps)
|
||||
min_inv_rho = sigma_min**(1 / rho)
|
||||
max_inv_rho = sigma_max**(1 / rho)
|
||||
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho))**rho
|
||||
# Convert EDM sigma to flow-matching sigma in [0, 1).
|
||||
sigmas = sigmas / (1.0 + sigmas)
|
||||
else:
|
||||
if sigmas is None:
|
||||
assert num_inference_steps is not None
|
||||
sigmas = np.linspace(self.sigma_max, self.sigma_min,
|
||||
num_inference_steps +
|
||||
1).copy()[:-1] # pyright: ignore
|
||||
if sigmas is None:
|
||||
assert num_inference_steps is not None
|
||||
sigmas = np.linspace(self.sigma_max, self.sigma_min,
|
||||
num_inference_steps +
|
||||
1).copy()[:-1] # pyright: ignore
|
||||
|
||||
if self.config.use_dynamic_shifting:
|
||||
assert mu is not None
|
||||
sigmas = self.time_shift(mu, 1.0, sigmas) # pyright: ignore
|
||||
else:
|
||||
if not use_karras_sigmas:
|
||||
if shift is None:
|
||||
shift = self.config.shift
|
||||
assert isinstance(sigmas, np.ndarray)
|
||||
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) # pyright: ignore
|
||||
if shift is None:
|
||||
shift = self.config.shift
|
||||
assert isinstance(sigmas, np.ndarray)
|
||||
sigmas = shift * sigmas / (1 +
|
||||
(shift - 1) * sigmas) # pyright: ignore
|
||||
|
||||
if self.config.final_sigmas_type == "sigma_min":
|
||||
sigma_last = ((1 - self.alphas_cumprod[0]) /
|
||||
@@ -446,12 +418,8 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
|
||||
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
|
||||
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
|
||||
|
||||
# Numerical safety
|
||||
eps = 1e-12
|
||||
lambda_t = torch.log(torch.clamp(alpha_t, min=eps)) - torch.log(
|
||||
torch.clamp(sigma_t, min=eps))
|
||||
lambda_s0 = torch.log(torch.clamp(alpha_s0, min=eps)) - torch.log(
|
||||
torch.clamp(sigma_s0, min=eps))
|
||||
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
|
||||
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
|
||||
|
||||
h = lambda_t - lambda_s0
|
||||
device = sample.device
|
||||
@@ -462,8 +430,7 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
|
||||
si = self.step_index - i # pyright: ignore
|
||||
mi = model_output_list[-(i + 1)]
|
||||
alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
|
||||
lambda_si = torch.log(torch.clamp(alpha_si, min=eps)) - torch.log(
|
||||
torch.clamp(sigma_si, min=eps))
|
||||
lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
|
||||
rk = (lambda_si - lambda_s0) / h
|
||||
rks.append(rk)
|
||||
assert mi is not None
|
||||
@@ -596,11 +563,8 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
|
||||
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
|
||||
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
|
||||
|
||||
eps = 1e-12
|
||||
lambda_t = torch.log(torch.clamp(alpha_t, min=eps)) - torch.log(
|
||||
torch.clamp(sigma_t, min=eps))
|
||||
lambda_s0 = torch.log(torch.clamp(alpha_s0, min=eps)) - torch.log(
|
||||
torch.clamp(sigma_s0, min=eps))
|
||||
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
|
||||
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
|
||||
|
||||
h = lambda_t - lambda_s0
|
||||
device = this_sample.device
|
||||
@@ -611,8 +575,7 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
|
||||
si = self.step_index - (i + 1) # pyright: ignore
|
||||
mi = model_output_list[-(i + 1)]
|
||||
alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
|
||||
lambda_si = torch.log(torch.clamp(alpha_si, min=eps)) - torch.log(
|
||||
torch.clamp(sigma_si, min=eps))
|
||||
lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
|
||||
rk = (lambda_si - lambda_s0) / h
|
||||
rks.append(rk)
|
||||
assert mi is not None
|
||||
|
||||
@@ -1,735 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
Cosmos 2.5 / Wan2.1 VAE adapter.
|
||||
|
||||
Why this exists:
|
||||
- Cosmos2.5 uses a Wan2.1-style VAE, but the *diffusion model* operates in a
|
||||
**normalized latent space**:
|
||||
z_norm = (z - mean) / std
|
||||
|
||||
Meanwhile, FastVideo's `AutoencoderKLWan` operates in the VAE's native latent
|
||||
space (denormalized):
|
||||
z = z_norm * std + mean
|
||||
|
||||
This adapter provides a single, stable interface for FastVideo pipelines:
|
||||
- `encode(x)` returns an object with `.mean` / `.sample()` / `.mode()`
|
||||
- `decode(z)` returns a tensor in pixel space
|
||||
|
||||
It also exposes flags used by pipeline stages to avoid double (de)normalization:
|
||||
- `handles_latent_norm = True` -> stages should NOT normalize encoder latents
|
||||
- `handles_latent_denorm = True` -> stages should NOT denormalize before decode
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
|
||||
|
||||
@dataclass
|
||||
class _TensorLatentDist:
|
||||
"""Minimal distribution-like wrapper used by pipeline stages."""
|
||||
|
||||
mean: torch.Tensor
|
||||
|
||||
def mode(self) -> torch.Tensor:
|
||||
return self.mean
|
||||
|
||||
def sample(self, generator: Any | None = None) -> torch.Tensor: # generator for API compatibility
|
||||
# The official interface encodes deterministically; for compatibility we
|
||||
# return the mean. (Stochastic posterior sampling isn't required for
|
||||
# Cosmos2.5 inference.)
|
||||
_ = generator
|
||||
return self.mean
|
||||
|
||||
|
||||
class Cosmos25WanVAEAdapter(nn.Module):
|
||||
"""
|
||||
Adapter that makes a Wan2.1-style VAE follow Cosmos2.5's latent contract:
|
||||
- `encode()` returns **normalized** latents
|
||||
- `decode()` expects **normalized** latents
|
||||
"""
|
||||
|
||||
# Pipeline stage hints (see latent_preparation.py / decoding.py / image_encoding.py)
|
||||
handles_latent_norm: bool = True
|
||||
handles_latent_denorm: bool = True
|
||||
latent_norm_mode: str = "internal" # informational
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
inner: Any,
|
||||
*,
|
||||
latents_mean: Optional[torch.Tensor] = None,
|
||||
latents_std: Optional[torch.Tensor] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.inner = inner
|
||||
|
||||
# Preserve `config` when available; some pipeline utilities expect it.
|
||||
self.config = getattr(inner, "config", None)
|
||||
|
||||
# If not provided, try to derive from `config.latents_mean/std`.
|
||||
cfg = self.config
|
||||
if latents_mean is None and cfg is not None and hasattr(cfg, "latents_mean"):
|
||||
latents_mean = torch.tensor(cfg.latents_mean, dtype=torch.float32).view(1, -1, 1, 1, 1)
|
||||
if latents_std is None and cfg is not None and hasattr(cfg, "latents_std"):
|
||||
latents_std = torch.tensor(cfg.latents_std, dtype=torch.float32).view(1, -1, 1, 1, 1)
|
||||
|
||||
if latents_mean is None or latents_std is None:
|
||||
raise RuntimeError(
|
||||
"Cosmos25WanVAEAdapter requires latents_mean/latents_std (either passed explicitly or available on inner.config)."
|
||||
)
|
||||
|
||||
# Register as buffers so `.to(...)` moves them with the module.
|
||||
self.register_buffer("_latents_mean", latents_mean, persistent=False)
|
||||
self.register_buffer("_latents_std", latents_std, persistent=False)
|
||||
|
||||
def _to_latent_stats(self, like: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
mean = self._latents_mean.to(device=like.device, dtype=like.dtype)
|
||||
std = self._latents_std.to(device=like.device, dtype=like.dtype)
|
||||
return mean, std
|
||||
|
||||
def get_latent_num_frames(self, num_pixel_frames: int) -> int:
|
||||
# Keep parity with official interface.
|
||||
if hasattr(self.inner, "get_latent_num_frames"):
|
||||
return int(self.inner.get_latent_num_frames(num_pixel_frames))
|
||||
return 1 + (num_pixel_frames - 1) // 4
|
||||
|
||||
def encode(self, x: torch.Tensor) -> _TensorLatentDist:
|
||||
"""
|
||||
Returns *normalized* latents (Cosmos contract).
|
||||
"""
|
||||
enc_out = self.inner.encode(x)
|
||||
|
||||
# Support common encoder output shapes:
|
||||
# - DiagonalGaussianDistribution (FastVideo VAE): has `.mean` / `.sample()` / `.mode()`
|
||||
# - diffusers EncoderOutput: has `.latent_dist`
|
||||
# - raw tensor
|
||||
if hasattr(enc_out, "latent_dist"):
|
||||
dist = enc_out.latent_dist
|
||||
z_mean = dist.mode() if hasattr(dist, "mode") else dist.mean
|
||||
elif hasattr(enc_out, "mode") and hasattr(enc_out, "mean"):
|
||||
z_mean = enc_out.mode()
|
||||
elif isinstance(enc_out, torch.Tensor):
|
||||
z_mean = enc_out
|
||||
else:
|
||||
attrs = [a for a in dir(enc_out) if not a.startswith("_")]
|
||||
raise RuntimeError(
|
||||
f"Unsupported VAE encoder output type: {type(enc_out)}. attrs={attrs}"
|
||||
)
|
||||
|
||||
mean, std = self._to_latent_stats(z_mean)
|
||||
z_norm = (z_mean - mean) / std
|
||||
return _TensorLatentDist(z_norm)
|
||||
|
||||
def decode(self, z: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Expects *normalized* latents (Cosmos contract).
|
||||
"""
|
||||
mean, std = self._to_latent_stats(z)
|
||||
z_denorm = z * std + mean
|
||||
out = self.inner.decode(z_denorm)
|
||||
return out.sample if hasattr(out, "sample") else out
|
||||
|
||||
|
||||
#
|
||||
# Official-like Wan2.1 VAE implementation (ported from cosmos_predict2 wan2pt1.py)
|
||||
# -------------------------------------------------------------------------------
|
||||
# Motivation:
|
||||
# - We already solved checkpoint *key mapping* and can load official weights.
|
||||
# - Remaining output drift vs the official tokenizer is largely decoder-side.
|
||||
# - FastVideo's `AutoencoderKLWan` uses a different temporal upsample path
|
||||
# (`DupUp3D` + `first_chunk` slicing), while the official tokenizer uses
|
||||
# `Resample(mode="upsample3d")` with a time-conv + interleave reshape.
|
||||
#
|
||||
# This section ports the core modules (CausalConv3d/Resample/etc.) so we can run
|
||||
# a VAE that is behaviorally closer to the official implementation WITHOUT
|
||||
# importing any official repo classes at runtime.
|
||||
#
|
||||
|
||||
CACHE_T = 2
|
||||
|
||||
|
||||
class Cosmos25CausalConv3d(nn.Conv3d):
|
||||
"""
|
||||
Official-like causal 3D convolution.
|
||||
|
||||
Matches `CausalConv3d` in the official tokenizer: uses explicit F.pad and
|
||||
supports a `cache_x` prefix for causal chunking.
|
||||
"""
|
||||
|
||||
def __init__(self, *args: Any, **kwargs: Any) -> None:
|
||||
super().__init__(*args, **kwargs)
|
||||
# padding order for F.pad: (W_left, W_right, H_left, H_right, T_left, T_right)
|
||||
self._padding: tuple[int, ...] = (
|
||||
self.padding[2],
|
||||
self.padding[2],
|
||||
self.padding[1],
|
||||
self.padding[1],
|
||||
2 * self.padding[0],
|
||||
0,
|
||||
)
|
||||
self.padding = (0, 0, 0)
|
||||
|
||||
def forward(self, x: torch.Tensor, cache_x: torch.Tensor | None = None) -> torch.Tensor:
|
||||
padding = list(self._padding)
|
||||
if cache_x is not None and self._padding[4] > 0:
|
||||
cache_x = cache_x.to(x.device)
|
||||
x = torch.cat([cache_x, x], dim=2)
|
||||
padding[4] -= cache_x.shape[2]
|
||||
x = F.pad(x, padding)
|
||||
return super().forward(x)
|
||||
|
||||
|
||||
class Cosmos25RMSNorm(nn.Module):
|
||||
"""Official-like RMS_norm (uses learnable gamma and optional bias)."""
|
||||
|
||||
def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None:
|
||||
super().__init__()
|
||||
broadcastable_dims = (1, 1, 1) if not images else (1, 1)
|
||||
shape = (dim, *broadcastable_dims) if channel_first else (dim,)
|
||||
|
||||
self.channel_first = channel_first
|
||||
self.scale = dim**0.5
|
||||
self.gamma = nn.Parameter(torch.ones(shape))
|
||||
self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
dim = 1 if self.channel_first else -1
|
||||
return F.normalize(x, dim=dim) * self.scale * self.gamma + self.bias
|
||||
|
||||
|
||||
class Cosmos25Upsample(nn.Upsample):
|
||||
"""Official-like Upsample that is safe for bf16 (casts to fp32 internally)."""
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor: # type: ignore[override]
|
||||
return super().forward(x.float()).type_as(x)
|
||||
|
||||
|
||||
class Cosmos25Resample(nn.Module):
|
||||
"""
|
||||
Official-like Resample used for both spatial and temporal up/downsampling.
|
||||
"""
|
||||
|
||||
def __init__(self, dim: int, mode: str) -> None:
|
||||
assert mode in ("none", "upsample2d", "upsample3d", "downsample2d", "downsample3d")
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.mode = mode
|
||||
|
||||
if mode == "upsample2d":
|
||||
self.resample = nn.Sequential(
|
||||
Cosmos25Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
|
||||
nn.Conv2d(dim, dim // 2, 3, padding=1),
|
||||
)
|
||||
elif mode == "upsample3d":
|
||||
self.resample = nn.Sequential(
|
||||
Cosmos25Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
|
||||
nn.Conv2d(dim, dim // 2, 3, padding=1),
|
||||
)
|
||||
self.time_conv = Cosmos25CausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
|
||||
elif mode == "downsample2d":
|
||||
self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
|
||||
elif mode == "downsample3d":
|
||||
self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
|
||||
self.time_conv = Cosmos25CausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
|
||||
else:
|
||||
self.resample = nn.Identity()
|
||||
|
||||
def forward(self, x: torch.Tensor, feat_cache: list[Any] | None = None, feat_idx: list[int] = [0]) -> torch.Tensor:
|
||||
b, c, t, h, w = x.size()
|
||||
|
||||
# Temporal upsample uses a time-conv and then interleaves frames.
|
||||
if self.mode == "upsample3d" and feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
if feat_cache[idx] is None:
|
||||
feat_cache[idx] = "Rep"
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep":
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep":
|
||||
cache_x = torch.cat([torch.zeros_like(cache_x).to(cache_x.device), cache_x], dim=2)
|
||||
|
||||
if feat_cache[idx] == "Rep":
|
||||
x = self.time_conv(x)
|
||||
else:
|
||||
x = self.time_conv(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
|
||||
x = x.reshape(b, 2, c, t, h, w)
|
||||
x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3)
|
||||
x = x.reshape(b, c, t * 2, h, w)
|
||||
|
||||
t = x.shape[2]
|
||||
x = rearrange(x, "b c t h w -> (b t) c h w")
|
||||
x = self.resample(x)
|
||||
x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
|
||||
|
||||
# Temporal downsample: time_conv consumes last-frame cache.
|
||||
if self.mode == "downsample3d" and feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
if feat_cache[idx] is None:
|
||||
feat_cache[idx] = x.clone()
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
cache_x = x[:, :, -1:, :, :].clone()
|
||||
x = self.time_conv(torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Cosmos25ResidualBlock(nn.Module):
|
||||
def __init__(self, in_dim: int, out_dim: int, dropout: float = 0.0) -> None:
|
||||
super().__init__()
|
||||
self.in_dim = in_dim
|
||||
self.out_dim = out_dim
|
||||
self.residual = nn.Sequential(
|
||||
Cosmos25RMSNorm(in_dim, images=False),
|
||||
nn.SiLU(),
|
||||
Cosmos25CausalConv3d(in_dim, out_dim, 3, padding=1),
|
||||
Cosmos25RMSNorm(out_dim, images=False),
|
||||
nn.SiLU(),
|
||||
nn.Dropout(dropout),
|
||||
Cosmos25CausalConv3d(out_dim, out_dim, 3, padding=1),
|
||||
)
|
||||
self.shortcut = Cosmos25CausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()
|
||||
|
||||
def forward(self, x: torch.Tensor, feat_cache: list[Any] | None = None, feat_idx: list[int] = [0]) -> torch.Tensor:
|
||||
h = self.shortcut(x)
|
||||
for layer in self.residual:
|
||||
if isinstance(layer, Cosmos25CausalConv3d) and feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = layer(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x)
|
||||
return x + h
|
||||
|
||||
|
||||
class Cosmos25AttentionBlock(nn.Module):
|
||||
"""Official-like causal self-attention with a single head."""
|
||||
|
||||
def __init__(self, dim: int) -> None:
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.norm = Cosmos25RMSNorm(dim)
|
||||
self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
|
||||
self.proj = nn.Conv2d(dim, dim, 1)
|
||||
nn.init.zeros_(self.proj.weight)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
identity = x
|
||||
b, c, t, h, w = x.size()
|
||||
x2 = rearrange(x, "b c t h w -> (b t) c h w")
|
||||
x2 = self.norm(x2)
|
||||
q, k, v = (
|
||||
self.to_qkv(x2)
|
||||
.reshape(b * t, 1, c * 3, -1)
|
||||
.permute(0, 1, 3, 2)
|
||||
.contiguous()
|
||||
.chunk(3, dim=-1)
|
||||
)
|
||||
x2 = F.scaled_dot_product_attention(q, k, v)
|
||||
x2 = x2.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w)
|
||||
x2 = self.proj(x2)
|
||||
x2 = rearrange(x2, "(b t) c h w-> b c t h w", t=t)
|
||||
return x2 + identity
|
||||
|
||||
|
||||
class Cosmos25Encoder3d(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int = 96,
|
||||
z_dim: int = 32,
|
||||
dim_mult: list[int] = [1, 2, 4, 4],
|
||||
num_res_blocks: int = 2,
|
||||
attn_scales: list[float] = [],
|
||||
temperal_downsample: list[bool] = [False, True, True],
|
||||
dropout: float = 0.0,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
dims = [dim * u for u in [1] + dim_mult]
|
||||
scale = 1.0
|
||||
|
||||
self.conv1 = Cosmos25CausalConv3d(3, dims[0], 3, padding=1)
|
||||
|
||||
downsamples: list[nn.Module] = []
|
||||
for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
|
||||
for _ in range(num_res_blocks):
|
||||
downsamples.append(Cosmos25ResidualBlock(in_dim, out_dim, dropout))
|
||||
if scale in attn_scales:
|
||||
downsamples.append(Cosmos25AttentionBlock(out_dim))
|
||||
in_dim = out_dim
|
||||
|
||||
if i != len(dim_mult) - 1:
|
||||
mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
|
||||
downsamples.append(Cosmos25Resample(out_dim, mode=mode))
|
||||
scale /= 2.0
|
||||
self.downsamples = nn.Sequential(*downsamples)
|
||||
|
||||
self.middle = nn.Sequential(
|
||||
Cosmos25ResidualBlock(out_dim, out_dim, dropout),
|
||||
Cosmos25AttentionBlock(out_dim),
|
||||
Cosmos25ResidualBlock(out_dim, out_dim, dropout),
|
||||
)
|
||||
|
||||
self.head = nn.Sequential(
|
||||
Cosmos25RMSNorm(out_dim, images=False),
|
||||
nn.SiLU(),
|
||||
Cosmos25CausalConv3d(out_dim, z_dim, 3, padding=1),
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor, feat_cache: list[Any] | None = None, feat_idx: list[int] = [0]) -> torch.Tensor:
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = self.conv1(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv1(x)
|
||||
|
||||
for layer in self.downsamples:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx) # type: ignore[misc]
|
||||
else:
|
||||
x = layer(x) # type: ignore[misc]
|
||||
|
||||
for layer in self.middle:
|
||||
if isinstance(layer, Cosmos25ResidualBlock) and feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x) # type: ignore[misc]
|
||||
|
||||
for layer in self.head:
|
||||
if isinstance(layer, Cosmos25CausalConv3d) and feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = layer(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x) # type: ignore[misc]
|
||||
return x
|
||||
|
||||
|
||||
class Cosmos25Decoder3d(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int = 96,
|
||||
z_dim: int = 16,
|
||||
dim_mult: list[int] = [1, 2, 4, 4],
|
||||
num_res_blocks: int = 2,
|
||||
attn_scales: list[float] = [],
|
||||
temperal_upsample: list[bool] = [False, True, True],
|
||||
dropout: float = 0.0,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
|
||||
scale = 1.0 / 2 ** (len(dim_mult) - 2)
|
||||
|
||||
self.conv1 = Cosmos25CausalConv3d(z_dim, dims[0], 3, padding=1)
|
||||
self.middle = nn.Sequential(
|
||||
Cosmos25ResidualBlock(dims[0], dims[0], dropout),
|
||||
Cosmos25AttentionBlock(dims[0]),
|
||||
Cosmos25ResidualBlock(dims[0], dims[0], dropout),
|
||||
)
|
||||
|
||||
upsamples: list[nn.Module] = []
|
||||
for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
|
||||
if i in (1, 2, 3):
|
||||
in_dim = in_dim // 2
|
||||
for _ in range(num_res_blocks + 1):
|
||||
upsamples.append(Cosmos25ResidualBlock(in_dim, out_dim, dropout))
|
||||
if scale in attn_scales:
|
||||
upsamples.append(Cosmos25AttentionBlock(out_dim))
|
||||
in_dim = out_dim
|
||||
if i != len(dim_mult) - 1:
|
||||
mode = "upsample3d" if temperal_upsample[i] else "upsample2d"
|
||||
upsamples.append(Cosmos25Resample(out_dim, mode=mode))
|
||||
scale *= 2.0
|
||||
self.upsamples = nn.Sequential(*upsamples)
|
||||
|
||||
self.head = nn.Sequential(
|
||||
Cosmos25RMSNorm(out_dim, images=False),
|
||||
nn.SiLU(),
|
||||
Cosmos25CausalConv3d(out_dim, 3, 3, padding=1),
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor, feat_cache: list[Any] | None = None, feat_idx: list[int] = [0]) -> torch.Tensor:
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = self.conv1(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv1(x)
|
||||
|
||||
for layer in self.middle:
|
||||
if isinstance(layer, Cosmos25ResidualBlock) and feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x) # type: ignore[misc]
|
||||
|
||||
for layer in self.upsamples:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx) # type: ignore[misc]
|
||||
else:
|
||||
x = layer(x) # type: ignore[misc]
|
||||
|
||||
for layer in self.head:
|
||||
if isinstance(layer, Cosmos25CausalConv3d) and feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = layer(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x) # type: ignore[misc]
|
||||
return x
|
||||
|
||||
|
||||
def _count_cosmos25_conv3d(model: nn.Module) -> int:
|
||||
return sum(1 for m in model.modules() if isinstance(m, Cosmos25CausalConv3d))
|
||||
|
||||
|
||||
class Cosmos25WanVAE(nn.Module):
|
||||
"""
|
||||
A FastVideo-native copy of the *official-like* Wan2.1 VAE core.
|
||||
|
||||
Key properties:
|
||||
- Module naming matches official tokenizer (`encoder`, `decoder`, `conv1`, `conv2`)
|
||||
so it can consume `tokenizer.pth` keys directly.
|
||||
- `encode()` returns **normalized** latents and `decode()` expects **normalized**
|
||||
latents (Cosmos2.5 contract), matching `Wan2pt1VAEInterface`.
|
||||
"""
|
||||
|
||||
handles_latent_norm: bool = True
|
||||
handles_latent_denorm: bool = True
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
device: torch.device | str = "cpu",
|
||||
dtype: torch.dtype = torch.float32,
|
||||
temporal_window: int = 4,
|
||||
latents_mean: Optional[torch.Tensor] = None,
|
||||
latents_std: Optional[torch.Tensor] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
# Official hyperparams for Cosmos2.5 tokenizer (Wan2.1 VAE).
|
||||
cfg = dict(
|
||||
dim=96,
|
||||
z_dim=16,
|
||||
dim_mult=[1, 2, 4, 4],
|
||||
num_res_blocks=2,
|
||||
attn_scales=[],
|
||||
temperal_downsample=[False, True, True],
|
||||
dropout=0.0,
|
||||
temporal_window=temporal_window,
|
||||
)
|
||||
self.z_dim = 16
|
||||
self.temporal_window = temporal_window
|
||||
|
||||
self.encoder = Cosmos25Encoder3d(
|
||||
dim=cfg["dim"],
|
||||
z_dim=cfg["z_dim"] * 2,
|
||||
dim_mult=cfg["dim_mult"],
|
||||
num_res_blocks=cfg["num_res_blocks"],
|
||||
attn_scales=cfg["attn_scales"],
|
||||
temperal_downsample=cfg["temperal_downsample"],
|
||||
dropout=cfg["dropout"],
|
||||
)
|
||||
self.conv1 = Cosmos25CausalConv3d(self.z_dim * 2, self.z_dim * 2, 1)
|
||||
self.conv2 = Cosmos25CausalConv3d(self.z_dim, self.z_dim, 1)
|
||||
self.decoder = Cosmos25Decoder3d(
|
||||
dim=cfg["dim"],
|
||||
z_dim=cfg["z_dim"],
|
||||
dim_mult=cfg["dim_mult"],
|
||||
num_res_blocks=cfg["num_res_blocks"],
|
||||
attn_scales=cfg["attn_scales"],
|
||||
temperal_upsample=list(cfg["temperal_downsample"])[::-1],
|
||||
dropout=cfg["dropout"],
|
||||
)
|
||||
|
||||
# Default Cosmos2.5 latent stats (shared with configs).
|
||||
if latents_mean is None:
|
||||
latents_mean = torch.tensor(
|
||||
[
|
||||
-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,
|
||||
],
|
||||
dtype=torch.float32,
|
||||
).view(1, 16, 1, 1, 1)
|
||||
if latents_std is None:
|
||||
latents_std = torch.tensor(
|
||||
[
|
||||
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,
|
||||
],
|
||||
dtype=torch.float32,
|
||||
).view(1, 16, 1, 1, 1)
|
||||
|
||||
self.register_buffer("_latents_mean", latents_mean, persistent=False)
|
||||
self.register_buffer("_latents_std", latents_std, persistent=False)
|
||||
|
||||
self.to(device=device, dtype=dtype)
|
||||
self.clear_cache()
|
||||
|
||||
def clear_cache(self) -> None:
|
||||
# Decoder cache
|
||||
self._conv_num = _count_cosmos25_conv3d(self.decoder)
|
||||
self._conv_idx = [0]
|
||||
self._feat_map: list[Any] = [None] * self._conv_num
|
||||
# Encoder cache
|
||||
self._enc_conv_num = _count_cosmos25_conv3d(self.encoder)
|
||||
self._enc_conv_idx = [0]
|
||||
self._enc_feat_map: list[Any] = [None] * self._enc_conv_num
|
||||
|
||||
def _scale(self, like: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
mean = self._latents_mean.to(device=like.device, dtype=like.dtype)
|
||||
std = self._latents_std.to(device=like.device, dtype=like.dtype)
|
||||
return mean, 1.0 / std
|
||||
|
||||
def _i0_encode(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return self.encoder(x[:, :, :1, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx)
|
||||
|
||||
def _i0_decode(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return self.decoder(x[:, :, 0:1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
|
||||
|
||||
def encode(self, x: torch.Tensor) -> _TensorLatentDist:
|
||||
"""
|
||||
Encode to *normalized* latents (Cosmos contract).
|
||||
"""
|
||||
self.clear_cache()
|
||||
t = x.shape[2]
|
||||
iters = 1 + (t - 1) // self.temporal_window
|
||||
|
||||
for i in range(iters):
|
||||
self._enc_conv_idx = [0]
|
||||
if i == 0:
|
||||
out = self._i0_encode(x)
|
||||
else:
|
||||
out_ = self.encoder(
|
||||
x[:, :, 1 + self.temporal_window * (i - 1) : 1 + self.temporal_window * i, :, :],
|
||||
feat_cache=self._enc_feat_map,
|
||||
feat_idx=self._enc_conv_idx,
|
||||
)
|
||||
out = torch.cat([out, out_], 2)
|
||||
|
||||
if (t - 1) % self.temporal_window:
|
||||
self._enc_conv_idx = [0]
|
||||
out_ = self.encoder(
|
||||
x[:, :, 1 + self.temporal_window * (iters - 1) :, :, :],
|
||||
feat_cache=self._enc_feat_map,
|
||||
feat_idx=self._enc_conv_idx,
|
||||
)
|
||||
out = torch.cat([out, out_], 2)
|
||||
|
||||
mu, _log_var = self.conv1(out).chunk(2, dim=1)
|
||||
mean, inv_std = self._scale(mu)
|
||||
z_norm = (mu - mean) * inv_std
|
||||
self.clear_cache()
|
||||
return _TensorLatentDist(z_norm)
|
||||
|
||||
def decode(self, latent: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Decode from *normalized* latents (Cosmos contract).
|
||||
"""
|
||||
self.clear_cache()
|
||||
mean, inv_std = self._scale(latent)
|
||||
z = latent / inv_std + mean # z = z_norm * std + mean
|
||||
|
||||
iter_ = z.shape[2]
|
||||
x = self.conv2(z)
|
||||
for i in range(iter_):
|
||||
self._conv_idx = [0]
|
||||
if i == 0:
|
||||
out = self._i0_decode(x)
|
||||
else:
|
||||
out_ = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
|
||||
out = torch.cat([out, out_], 2)
|
||||
|
||||
self.clear_cache()
|
||||
return out
|
||||
|
||||
# --- Interface helpers (match official Wan2pt1VAEInterface) ---
|
||||
def get_latent_num_frames(self, num_pixel_frames: int) -> int:
|
||||
return 1 + (int(num_pixel_frames) - 1) // 4
|
||||
|
||||
def get_pixel_num_frames(self, num_latent_frames: int) -> int:
|
||||
return (int(num_latent_frames) - 1) * 4 + 1
|
||||
|
||||
@property
|
||||
def spatial_compression_factor(self) -> int:
|
||||
return 8
|
||||
|
||||
@property
|
||||
def temporal_compression_factor(self) -> int:
|
||||
return 4
|
||||
|
||||
@property
|
||||
def latent_ch(self) -> int:
|
||||
return 16
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,61 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""Cosmos 2.5 pipeline entry (staged pipeline)."""
|
||||
|
||||
from fastvideo.fastvideo_args import FastVideoArgs
|
||||
from fastvideo.logger import init_logger
|
||||
from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
|
||||
from fastvideo.pipelines.stages import (ConditioningStage,
|
||||
Cosmos25DenoisingStage,
|
||||
Cosmos25LatentPreparationStage,
|
||||
DecodingStage, InputValidationStage,
|
||||
Cosmos25TextEncodingStage,
|
||||
Cosmos25TimestepPreparationStage)
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class Cosmos2_5Pipeline(ComposedPipelineBase):
|
||||
"""Cosmos 2.5 video generation pipeline."""
|
||||
|
||||
_required_config_modules = [
|
||||
"text_encoder", "tokenizer", "vae", "transformer", "scheduler",
|
||||
"safety_checker"
|
||||
]
|
||||
|
||||
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
|
||||
logger.info("Creating Cosmos 2.5 pipeline stages...")
|
||||
|
||||
self.add_stage(stage_name="input_validation_stage",
|
||||
stage=InputValidationStage())
|
||||
|
||||
self.add_stage(
|
||||
stage_name="prompt_encoding_stage",
|
||||
stage=Cosmos25TextEncodingStage(
|
||||
text_encoder=self.get_module("text_encoder"), ),
|
||||
)
|
||||
|
||||
self.add_stage(stage_name="conditioning_stage",
|
||||
stage=ConditioningStage())
|
||||
|
||||
self.add_stage(stage_name="timestep_preparation_stage",
|
||||
stage=Cosmos25TimestepPreparationStage(
|
||||
scheduler=self.get_module("scheduler")))
|
||||
|
||||
self.add_stage(stage_name="latent_preparation_stage",
|
||||
stage=Cosmos25LatentPreparationStage(
|
||||
scheduler=self.get_module("scheduler"),
|
||||
transformer=self.get_module("transformer"),
|
||||
vae=self.get_module("vae")))
|
||||
|
||||
self.add_stage(stage_name="denoising_stage",
|
||||
stage=Cosmos25DenoisingStage(
|
||||
transformer=self.get_module("transformer"),
|
||||
scheduler=self.get_module("scheduler")))
|
||||
|
||||
self.add_stage(stage_name="decoding_stage",
|
||||
stage=DecodingStage(vae=self.get_module("vae")))
|
||||
logger.info("Cosmos 2.5 pipeline stages created")
|
||||
|
||||
|
||||
# Entry point for pipeline registry
|
||||
EntryClass = Cosmos2_5Pipeline
|
||||
@@ -2,10 +2,5 @@
|
||||
"""LongCat pipeline module."""
|
||||
|
||||
from fastvideo.pipelines.basic.longcat.longcat_pipeline import LongCatPipeline
|
||||
from fastvideo.pipelines.basic.longcat.longcat_i2v_pipeline import LongCatImageToVideoPipeline
|
||||
from fastvideo.pipelines.basic.longcat.longcat_vc_pipeline import LongCatVideoContinuationPipeline
|
||||
|
||||
__all__ = [
|
||||
"LongCatPipeline", "LongCatImageToVideoPipeline",
|
||||
"LongCatVideoContinuationPipeline"
|
||||
]
|
||||
__all__ = ["LongCatPipeline"]
|
||||
|
||||
@@ -1,148 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
LongCat Image-to-Video pipeline implementation.
|
||||
|
||||
This module implements I2V (Image-to-Video) generation for LongCat using Tier 3
|
||||
conditioning with timestep masking, num_cond_latents support, and RoPE skipping.
|
||||
|
||||
Supports:
|
||||
- Basic I2V (50 steps, guidance_scale=4.0)
|
||||
- Distilled I2V with LoRA (16 steps, guidance_scale=1.0)
|
||||
- Refinement I2V for 720p upscaling (with refinement LoRA + BSA)
|
||||
"""
|
||||
|
||||
from fastvideo.fastvideo_args import FastVideoArgs
|
||||
from fastvideo.logger import init_logger
|
||||
from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
|
||||
from fastvideo.pipelines.stages import (
|
||||
DecodingStage,
|
||||
InputValidationStage,
|
||||
TextEncodingStage,
|
||||
TimestepPreparationStage,
|
||||
)
|
||||
from fastvideo.pipelines.stages.longcat_image_vae_encoding import LongCatImageVAEEncodingStage
|
||||
from fastvideo.pipelines.stages.longcat_i2v_latent_preparation import LongCatI2VLatentPreparationStage
|
||||
from fastvideo.pipelines.stages.longcat_i2v_denoising import LongCatI2VDenoisingStage
|
||||
from fastvideo.pipelines.stages.longcat_refine_init import LongCatRefineInitStage
|
||||
from fastvideo.pipelines.stages.longcat_refine_timestep import LongCatRefineTimestepStage
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class LongCatImageToVideoPipeline(LoRAPipeline, ComposedPipelineBase):
|
||||
"""
|
||||
LongCat Image-to-Video pipeline.
|
||||
|
||||
Generates video from a single input image using Tier 3 I2V conditioning:
|
||||
- Per-frame timestep masking (timestep[:, 0] = 0)
|
||||
- num_cond_latents parameter to transformer
|
||||
- RoPE skipping for conditioning frames
|
||||
- Selective denoising (skip first frame in scheduler)
|
||||
"""
|
||||
|
||||
_required_config_modules = [
|
||||
"text_encoder", "tokenizer", "vae", "transformer", "scheduler"
|
||||
]
|
||||
|
||||
def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
|
||||
"""Initialize LongCat-specific components."""
|
||||
# Same BSA initialization as base LongCat pipeline
|
||||
pipeline_config = fastvideo_args.pipeline_config
|
||||
transformer = self.get_module("transformer", None)
|
||||
if transformer is None:
|
||||
return
|
||||
|
||||
# Enable BSA if configured
|
||||
if pipeline_config.enable_bsa:
|
||||
bsa_params_cfg = getattr(pipeline_config, 'bsa_params', None) or {}
|
||||
sparsity = getattr(pipeline_config, 'bsa_sparsity', None)
|
||||
cdf_threshold = getattr(pipeline_config, 'bsa_cdf_threshold', None)
|
||||
chunk_q = getattr(pipeline_config, 'bsa_chunk_q', None)
|
||||
chunk_k = getattr(pipeline_config, 'bsa_chunk_k', None)
|
||||
|
||||
effective_bsa_params = dict(bsa_params_cfg) if isinstance(
|
||||
bsa_params_cfg, dict) else {}
|
||||
if sparsity is not None:
|
||||
effective_bsa_params['sparsity'] = sparsity
|
||||
if cdf_threshold is not None:
|
||||
effective_bsa_params['cdf_threshold'] = cdf_threshold
|
||||
if chunk_q is not None:
|
||||
effective_bsa_params['chunk_3d_shape_q'] = chunk_q
|
||||
if chunk_k is not None:
|
||||
effective_bsa_params['chunk_3d_shape_k'] = chunk_k
|
||||
|
||||
# Provide defaults
|
||||
effective_bsa_params.setdefault('sparsity', 0.9375)
|
||||
effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
|
||||
effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])
|
||||
|
||||
if hasattr(transformer, 'enable_bsa'):
|
||||
logger.info("Enabling BSA for LongCat I2V transformer")
|
||||
transformer.enable_bsa()
|
||||
if hasattr(transformer, 'blocks'):
|
||||
try:
|
||||
for blk in transformer.blocks:
|
||||
if hasattr(blk, 'self_attn'):
|
||||
blk.self_attn.bsa_params = effective_bsa_params
|
||||
except Exception as e:
|
||||
logger.warning("Failed to set BSA params: %s", e)
|
||||
logger.info("BSA parameters: %s", effective_bsa_params)
|
||||
else:
|
||||
if hasattr(transformer, 'disable_bsa'):
|
||||
transformer.disable_bsa()
|
||||
|
||||
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
|
||||
"""Set up I2V-specific pipeline stages."""
|
||||
|
||||
# 1. Input validation
|
||||
self.add_stage(stage_name="input_validation_stage",
|
||||
stage=InputValidationStage())
|
||||
|
||||
# 2. Text encoding (same as T2V)
|
||||
self.add_stage(stage_name="prompt_encoding_stage",
|
||||
stage=TextEncodingStage(
|
||||
text_encoders=[self.get_module("text_encoder")],
|
||||
tokenizers=[self.get_module("tokenizer")],
|
||||
))
|
||||
|
||||
# 3. Image VAE encoding (for I2V - skipped in refinement mode)
|
||||
self.add_stage(
|
||||
stage_name="image_vae_encoding_stage",
|
||||
stage=LongCatImageVAEEncodingStage(vae=self.get_module("vae")))
|
||||
|
||||
# 4. Refinement initialization (skipped if not refining)
|
||||
self.add_stage(stage_name="longcat_refine_init_stage",
|
||||
stage=LongCatRefineInitStage(vae=self.get_module("vae")))
|
||||
|
||||
# 5. Timestep preparation (generic)
|
||||
self.add_stage(stage_name="timestep_preparation_stage",
|
||||
stage=TimestepPreparationStage(
|
||||
scheduler=self.get_module("scheduler")))
|
||||
|
||||
# 6. Refinement timestep override (skipped if not refining)
|
||||
self.add_stage(stage_name="longcat_refine_timestep_stage",
|
||||
stage=LongCatRefineTimestepStage(
|
||||
scheduler=self.get_module("scheduler")))
|
||||
|
||||
# 7. Latent preparation with I2V conditioning
|
||||
self.add_stage(stage_name="latent_preparation_stage",
|
||||
stage=LongCatI2VLatentPreparationStage(
|
||||
scheduler=self.get_module("scheduler"),
|
||||
transformer=self.get_module("transformer")))
|
||||
|
||||
# 8. Denoising with I2V support
|
||||
self.add_stage(stage_name="denoising_stage",
|
||||
stage=LongCatI2VDenoisingStage(
|
||||
transformer=self.get_module("transformer"),
|
||||
transformer_2=self.get_module("transformer_2", None),
|
||||
scheduler=self.get_module("scheduler"),
|
||||
vae=self.get_module("vae"),
|
||||
pipeline=self))
|
||||
|
||||
# 9. Decoding
|
||||
self.add_stage(stage_name="decoding_stage",
|
||||
stage=DecodingStage(vae=self.get_module("vae"),
|
||||
pipeline=self))
|
||||
|
||||
|
||||
EntryClass = LongCatImageToVideoPipeline
|
||||
@@ -1,9 +1,9 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
LongCat video diffusion pipeline implementation.
|
||||
LongCat video diffusion pipeline implementation (Phase 1: Wrapper).
|
||||
|
||||
This module implements the LongCat video diffusion pipeline using FastVideo's
|
||||
modular pipeline architecture.
|
||||
This module contains a wrapper implementation of the LongCat video diffusion pipeline
|
||||
using FastVideo's modular pipeline architecture with the original LongCat modules.
|
||||
"""
|
||||
|
||||
from fastvideo.fastvideo_args import FastVideoArgs
|
||||
@@ -26,6 +26,9 @@ logger = init_logger(__name__)
|
||||
class LongCatPipeline(LoRAPipeline, ComposedPipelineBase):
|
||||
"""
|
||||
LongCat video diffusion pipeline with LoRA support.
|
||||
|
||||
Phase 1 implementation using wrapper modules from third_party/longcat_video.
|
||||
This validates the pipeline infrastructure before full FastVideo integration.
|
||||
"""
|
||||
|
||||
_required_config_modules = [
|
||||
|
||||
@@ -1,168 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
LongCat Video Continuation (VC) pipeline implementation.
|
||||
|
||||
This module implements VC (Video Continuation) generation for LongCat with
|
||||
KV cache optimization for 2-3x speedup.
|
||||
|
||||
Supports:
|
||||
- Basic VC (50 steps, guidance_scale=4.0)
|
||||
- Distilled VC with LoRA (16 steps, guidance_scale=1.0)
|
||||
- KV cache for conditioning frames
|
||||
"""
|
||||
|
||||
from fastvideo.fastvideo_args import FastVideoArgs
|
||||
from fastvideo.logger import init_logger
|
||||
from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
|
||||
from fastvideo.pipelines.stages import (
|
||||
DecodingStage,
|
||||
InputValidationStage,
|
||||
TextEncodingStage,
|
||||
TimestepPreparationStage,
|
||||
)
|
||||
from fastvideo.pipelines.stages.longcat_video_vae_encoding import LongCatVideoVAEEncodingStage
|
||||
from fastvideo.pipelines.stages.longcat_i2v_latent_preparation import LongCatI2VLatentPreparationStage
|
||||
from fastvideo.pipelines.stages.longcat_kv_cache_init import LongCatKVCacheInitStage
|
||||
from fastvideo.pipelines.stages.longcat_vc_denoising import LongCatVCDenoisingStage
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class LongCatVideoContinuationPipeline(LoRAPipeline, ComposedPipelineBase):
|
||||
"""
|
||||
LongCat Video Continuation pipeline.
|
||||
|
||||
Generates video continuation from multiple conditioning frames using
|
||||
optional KV cache for 2-3x speedup.
|
||||
|
||||
Key features:
|
||||
- Takes video input (13+ frames typically)
|
||||
- Encodes conditioning frames via VAE
|
||||
- Optionally pre-computes KV cache for conditioning
|
||||
- Uses cached K/V during denoising for speedup
|
||||
- Concatenates conditioning back after denoising
|
||||
"""
|
||||
|
||||
_required_config_modules = [
|
||||
"text_encoder", "tokenizer", "vae", "transformer", "scheduler"
|
||||
]
|
||||
|
||||
def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
|
||||
"""Initialize LongCat-specific components."""
|
||||
pipeline_config = fastvideo_args.pipeline_config
|
||||
transformer = self.get_module("transformer", None)
|
||||
if transformer is None:
|
||||
return
|
||||
|
||||
# Enable BSA if configured (for VC, BSA may not be needed)
|
||||
if getattr(pipeline_config, 'enable_bsa', False):
|
||||
bsa_params_cfg = getattr(pipeline_config, 'bsa_params', None) or {}
|
||||
sparsity = getattr(pipeline_config, 'bsa_sparsity', None)
|
||||
cdf_threshold = getattr(pipeline_config, 'bsa_cdf_threshold', None)
|
||||
chunk_q = getattr(pipeline_config, 'bsa_chunk_q', None)
|
||||
chunk_k = getattr(pipeline_config, 'bsa_chunk_k', None)
|
||||
|
||||
effective_bsa_params = dict(bsa_params_cfg) if isinstance(
|
||||
bsa_params_cfg, dict) else {}
|
||||
if sparsity is not None:
|
||||
effective_bsa_params['sparsity'] = sparsity
|
||||
if cdf_threshold is not None:
|
||||
effective_bsa_params['cdf_threshold'] = cdf_threshold
|
||||
if chunk_q is not None:
|
||||
effective_bsa_params['chunk_3d_shape_q'] = chunk_q
|
||||
if chunk_k is not None:
|
||||
effective_bsa_params['chunk_3d_shape_k'] = chunk_k
|
||||
|
||||
# Provide defaults
|
||||
effective_bsa_params.setdefault('sparsity', 0.9375)
|
||||
effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
|
||||
effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])
|
||||
|
||||
if hasattr(transformer, 'enable_bsa'):
|
||||
logger.info("Enabling BSA for LongCat VC transformer")
|
||||
transformer.enable_bsa()
|
||||
if hasattr(transformer, 'blocks'):
|
||||
try:
|
||||
for blk in transformer.blocks:
|
||||
if hasattr(blk, 'self_attn'):
|
||||
blk.self_attn.bsa_params = effective_bsa_params
|
||||
except Exception as e:
|
||||
logger.warning("Failed to set BSA params: %s", e)
|
||||
logger.info("BSA parameters: %s", effective_bsa_params)
|
||||
else:
|
||||
if hasattr(transformer, 'disable_bsa'):
|
||||
transformer.disable_bsa()
|
||||
|
||||
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
|
||||
"""Set up VC-specific pipeline stages."""
|
||||
|
||||
# 1. Input validation
|
||||
self.add_stage(stage_name="input_validation_stage",
|
||||
stage=InputValidationStage())
|
||||
|
||||
# 2. Text encoding
|
||||
self.add_stage(stage_name="prompt_encoding_stage",
|
||||
stage=TextEncodingStage(
|
||||
text_encoders=[self.get_module("text_encoder")],
|
||||
tokenizers=[self.get_module("tokenizer")],
|
||||
))
|
||||
|
||||
# 3. Video VAE encoding (encodes conditioning frames)
|
||||
self.add_stage(
|
||||
stage_name="video_vae_encoding_stage",
|
||||
stage=LongCatVideoVAEEncodingStage(vae=self.get_module("vae")))
|
||||
|
||||
# 4. Timestep preparation
|
||||
self.add_stage(stage_name="timestep_preparation_stage",
|
||||
stage=TimestepPreparationStage(
|
||||
scheduler=self.get_module("scheduler")))
|
||||
|
||||
# 5. Latent preparation (reuse I2V stage - it handles video_latent too)
|
||||
self.add_stage(stage_name="latent_preparation_stage",
|
||||
stage=LongCatVCLatentPreparationStage(
|
||||
scheduler=self.get_module("scheduler"),
|
||||
transformer=self.get_module("transformer")))
|
||||
|
||||
# 6. KV cache initialization (optional, based on config)
|
||||
# This is always added but will skip if use_kv_cache=False
|
||||
self.add_stage(stage_name="kv_cache_init_stage",
|
||||
stage=LongCatKVCacheInitStage(
|
||||
transformer=self.get_module("transformer")))
|
||||
|
||||
# 7. Denoising with VC and KV cache support
|
||||
self.add_stage(stage_name="denoising_stage",
|
||||
stage=LongCatVCDenoisingStage(
|
||||
transformer=self.get_module("transformer"),
|
||||
transformer_2=self.get_module("transformer_2", None),
|
||||
scheduler=self.get_module("scheduler"),
|
||||
vae=self.get_module("vae"),
|
||||
pipeline=self))
|
||||
|
||||
# 8. Decoding
|
||||
self.add_stage(stage_name="decoding_stage",
|
||||
stage=DecodingStage(vae=self.get_module("vae"),
|
||||
pipeline=self))
|
||||
|
||||
|
||||
class LongCatVCLatentPreparationStage(LongCatI2VLatentPreparationStage):
|
||||
"""
|
||||
Prepare latents with video conditioning for first N frames.
|
||||
|
||||
Extends I2V latent preparation to handle video_latent (multiple frames)
|
||||
instead of image_latent (single frame).
|
||||
"""
|
||||
|
||||
def forward(self, batch, fastvideo_args):
|
||||
"""Prepare latents with VC conditioning."""
|
||||
|
||||
# Check if we have video_latent (from VC encoding stage)
|
||||
video_latent = getattr(batch, 'video_latent', None)
|
||||
if video_latent is not None:
|
||||
# Set image_latent to video_latent for parent class compatibility
|
||||
batch.image_latent = video_latent
|
||||
|
||||
# Call parent class forward
|
||||
return super().forward(batch, fastvideo_args)
|
||||
|
||||
|
||||
EntryClass = LongCatVideoContinuationPipeline
|
||||
@@ -1,150 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
LTX-2 text-to-video pipeline.
|
||||
"""
|
||||
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
from fastvideo.fastvideo_args import FastVideoArgs
|
||||
from fastvideo.logger import init_logger
|
||||
from fastvideo.models.loader.component_loader import PipelineComponentLoader
|
||||
from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
|
||||
from fastvideo.pipelines.stages import (DecodingStage, InputValidationStage,
|
||||
LTX2AudioDecodingStage,
|
||||
LTX2DenoisingStage,
|
||||
LTX2LatentPreparationStage,
|
||||
TextEncodingStage)
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class LTX2Pipeline(ComposedPipelineBase):
|
||||
|
||||
_required_config_modules = [
|
||||
"text_encoder",
|
||||
"tokenizer",
|
||||
"transformer",
|
||||
"vae",
|
||||
"audio_vae",
|
||||
"vocoder",
|
||||
]
|
||||
|
||||
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
|
||||
self.add_stage(
|
||||
stage_name="input_validation_stage",
|
||||
stage=InputValidationStage(),
|
||||
)
|
||||
|
||||
self.add_stage(
|
||||
stage_name="prompt_encoding_stage",
|
||||
stage=TextEncodingStage(
|
||||
text_encoders=[self.get_module("text_encoder")],
|
||||
tokenizers=[self.get_module("tokenizer")],
|
||||
),
|
||||
)
|
||||
|
||||
self.add_stage(
|
||||
stage_name="latent_preparation_stage",
|
||||
stage=LTX2LatentPreparationStage(
|
||||
transformer=self.get_module("transformer"), ),
|
||||
)
|
||||
|
||||
self.add_stage(
|
||||
stage_name="denoising_stage",
|
||||
stage=LTX2DenoisingStage(
|
||||
transformer=self.get_module("transformer"), ),
|
||||
)
|
||||
|
||||
self.add_stage(
|
||||
stage_name="audio_decoding_stage",
|
||||
stage=LTX2AudioDecodingStage(
|
||||
audio_decoder=self.get_module("audio_vae"),
|
||||
vocoder=self.get_module("vocoder"),
|
||||
),
|
||||
)
|
||||
|
||||
self.add_stage(
|
||||
stage_name="decoding_stage",
|
||||
stage=DecodingStage(vae=self.get_module("vae")),
|
||||
)
|
||||
|
||||
def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
|
||||
tokenizer = self.get_module("tokenizer")
|
||||
if tokenizer is not None:
|
||||
tokenizer.padding_side = "left"
|
||||
if tokenizer.pad_token is None:
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
def load_modules(
|
||||
self,
|
||||
fastvideo_args: FastVideoArgs,
|
||||
loaded_modules: dict[str, Any] | None = None,
|
||||
) -> dict[str, Any]:
|
||||
model_index = self._load_config(self.model_path)
|
||||
logger.info("Loading pipeline modules from config: %s", model_index)
|
||||
|
||||
model_index.pop("_class_name")
|
||||
model_index.pop("_diffusers_version")
|
||||
model_index.pop("workload_type", None)
|
||||
|
||||
if len(model_index) <= 1:
|
||||
raise ValueError(
|
||||
"model_index.json must contain at least one pipeline module")
|
||||
|
||||
required_modules = self.required_config_modules
|
||||
modules: dict[str, Any] = {}
|
||||
|
||||
for module_name, module_spec in model_index.items():
|
||||
if not isinstance(module_spec, list) or len(module_spec) < 1:
|
||||
continue
|
||||
transformers_or_diffusers = module_spec[0]
|
||||
if transformers_or_diffusers is None:
|
||||
if module_name in self.required_config_modules:
|
||||
self.required_config_modules.remove(module_name)
|
||||
continue
|
||||
if module_name not in required_modules:
|
||||
continue
|
||||
if loaded_modules is not None and module_name in loaded_modules:
|
||||
modules[module_name] = loaded_modules[module_name]
|
||||
continue
|
||||
|
||||
component_model_path = os.path.join(self.model_path, module_name)
|
||||
if module_name == "tokenizer" and not os.path.isdir(
|
||||
component_model_path):
|
||||
gemma_path = os.path.join(self.model_path, "text_encoder",
|
||||
"gemma")
|
||||
if os.path.isdir(gemma_path):
|
||||
component_model_path = gemma_path
|
||||
else:
|
||||
raise ValueError(
|
||||
"Tokenizer directory missing and Gemma weights were not found."
|
||||
)
|
||||
|
||||
module = PipelineComponentLoader.load_module(
|
||||
module_name=module_name,
|
||||
component_model_path=component_model_path,
|
||||
transformers_or_diffusers=transformers_or_diffusers,
|
||||
fastvideo_args=fastvideo_args,
|
||||
)
|
||||
logger.info("Loaded module %s from %s", module_name,
|
||||
component_model_path)
|
||||
modules[module_name] = module
|
||||
|
||||
if "tokenizer" in required_modules and "tokenizer" not in modules:
|
||||
gemma_path = os.path.join(self.model_path, "text_encoder", "gemma")
|
||||
if os.path.isdir(gemma_path):
|
||||
modules["tokenizer"] = AutoTokenizer.from_pretrained(
|
||||
gemma_path, local_files_only=True)
|
||||
|
||||
for module_name in required_modules:
|
||||
if module_name not in modules or modules[module_name] is None:
|
||||
raise ValueError(
|
||||
f"Required module {module_name} was not loaded properly")
|
||||
|
||||
return modules
|
||||
|
||||
|
||||
EntryClass = LTX2Pipeline
|
||||
@@ -1,6 +0,0 @@
|
||||
from fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_pipeline import (
|
||||
TurboDiffusionPipeline, )
|
||||
from fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_i2v_pipeline import (
|
||||
TurboDiffusionI2VPipeline, )
|
||||
|
||||
__all__ = ["TurboDiffusionPipeline", "TurboDiffusionI2VPipeline"]
|
||||
|
||||
@@ -1,89 +0,0 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
TurboDiffusion I2V (Image-to-Video) Pipeline Implementation.
|
||||
|
||||
This module contains an implementation of the TurboDiffusion I2V pipeline
|
||||
for 1-4 step image-to-video generation using rCM (recurrent Consistency Model)
|
||||
sampling with SLA (Sparse-Linear Attention).
|
||||
|
||||
Key differences from T2V:
|
||||
- Uses dual models (high/low noise) with boundary switching
|
||||
- sigma_max=200 (vs 80 for T2V)
|
||||
- Mask conditioning with encoded first frame
|
||||
"""
|
||||
|
||||
from fastvideo.fastvideo_args import FastVideoArgs
|
||||
from fastvideo.logger import init_logger
|
||||
from fastvideo.models.schedulers.scheduling_rcm import RCMScheduler
|
||||
from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
|
||||
from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
|
||||
DenoisingStage, ImageVAEEncodingStage,
|
||||
InputValidationStage,
|
||||
LatentPreparationStage,
|
||||
TextEncodingStage,
|
||||
TimestepPreparationStage)
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class TurboDiffusionI2VPipeline(LoRAPipeline, ComposedPipelineBase):
|
||||
"""
|
||||
TurboDiffusion I2V pipeline for 1-4 step image-to-video generation.
|
||||
|
||||
Uses RCM scheduler, SLA attention, and dual model switching for
|
||||
high-quality I2V generation.
|
||||
"""
|
||||
|
||||
_required_config_modules = [
|
||||
"text_encoder", "tokenizer", "vae", "transformer", "transformer_2",
|
||||
"scheduler"
|
||||
]
|
||||
|
||||
def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
|
||||
# Use RCM scheduler with higher sigma_max for I2V
|
||||
logger.info(
|
||||
"Initializing RCM scheduler for TurboDiffusion I2V (sigma_max=200)")
|
||||
self.modules["scheduler"] = RCMScheduler(sigma_max=200.0)
|
||||
|
||||
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
|
||||
"""Set up pipeline stages with proper dependency injection."""
|
||||
|
||||
self.add_stage(stage_name="input_validation_stage",
|
||||
stage=InputValidationStage())
|
||||
|
||||
self.add_stage(stage_name="prompt_encoding_stage",
|
||||
stage=TextEncodingStage(
|
||||
text_encoders=[self.get_module("text_encoder")],
|
||||
tokenizers=[self.get_module("tokenizer")],
|
||||
))
|
||||
|
||||
self.add_stage(stage_name="conditioning_stage",
|
||||
stage=ConditioningStage())
|
||||
|
||||
self.add_stage(stage_name="timestep_preparation_stage",
|
||||
stage=TimestepPreparationStage(
|
||||
scheduler=self.get_module("scheduler")))
|
||||
|
||||
self.add_stage(stage_name="latent_preparation_stage",
|
||||
stage=LatentPreparationStage(
|
||||
scheduler=self.get_module("scheduler"),
|
||||
transformer=self.get_module("transformer", None)))
|
||||
|
||||
# I2V: Encode initial image to latent space
|
||||
self.add_stage(stage_name="image_latent_preparation_stage",
|
||||
stage=ImageVAEEncodingStage(vae=self.get_module("vae")))
|
||||
|
||||
self.add_stage(stage_name="denoising_stage",
|
||||
stage=DenoisingStage(
|
||||
transformer=self.get_module("transformer"),
|
||||
transformer_2=self.get_module("transformer_2", None),
|
||||
scheduler=self.get_module("scheduler"),
|
||||
vae=self.get_module("vae"),
|
||||
pipeline=self))
|
||||
|
||||
self.add_stage(stage_name="decoding_stage",
|
||||
stage=DecodingStage(vae=self.get_module("vae"),
|
||||
pipeline=self))
|
||||
|
||||
|
||||
EntryClass = TurboDiffusionI2VPipeline
|
||||
@@ -36,6 +36,11 @@ class TurboDiffusionPipeline(LoRAPipeline, ComposedPipelineBase):
|
||||
logger.info("Initializing RCM scheduler for TurboDiffusion")
|
||||
self.modules["scheduler"] = RCMScheduler(sigma_max=80.0)
|
||||
|
||||
# Store checkpoint path for later loading
|
||||
self._turbodiffusion_checkpoint = getattr(fastvideo_args,
|
||||
'turbodiffusion_checkpoint',
|
||||
None)
|
||||
|
||||
def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
|
||||
"""Set up pipeline stages with proper dependency injection."""
|
||||
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
from collections import defaultdict
|
||||
from collections.abc import Hashable
|
||||
from contextlib import nullcontext
|
||||
from typing import Any
|
||||
from collections.abc import Generator
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
@@ -14,13 +12,8 @@ from torch.distributed.tensor import DTensor
|
||||
|
||||
from fastvideo.distributed import get_local_torch_device
|
||||
from fastvideo.fastvideo_args import FastVideoArgs, TrainingArgs
|
||||
from fastvideo.hooks.hooks import ModuleHookManager
|
||||
from fastvideo.hooks.layerwise_offload import LayerwiseOffloadHook
|
||||
from fastvideo.layers.lora.linear import (
|
||||
BaseLayerWithLoRA,
|
||||
get_lora_layer,
|
||||
replace_submodule,
|
||||
)
|
||||
from fastvideo.layers.lora.linear import (BaseLayerWithLoRA, get_lora_layer,
|
||||
replace_submodule)
|
||||
from fastvideo.logger import init_logger
|
||||
from fastvideo.models.loader.utils import get_param_names_mapping
|
||||
from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
|
||||
@@ -29,89 +22,17 @@ from fastvideo.utils import maybe_download_lora
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
def _get_hook_ctx(module: nn.Module | None):
|
||||
if module is None:
|
||||
return nullcontext()
|
||||
hook_mgr = ModuleHookManager.get_from(module)
|
||||
if hook_mgr is not None:
|
||||
offload_hook = hook_mgr.forward_hooks.get(LayerwiseOffloadHook.name())
|
||||
if offload_hook is not None:
|
||||
return offload_hook.mutate_params_scope() # type: ignore
|
||||
return nullcontext()
|
||||
|
||||
|
||||
def _named_module_by_prefix(
|
||||
module: nn.Module, prefixes: list[str]
|
||||
) -> list[tuple[str | None, list[tuple[str, nn.Module]]]]:
|
||||
none_list: list[tuple[str, nn.Module]] = []
|
||||
prefix_list: list[tuple[str, list[tuple[str, nn.Module]]]] = [
|
||||
(prefix, []) for prefix in prefixes
|
||||
]
|
||||
for name, submodule in module.named_modules():
|
||||
for cur_prefix, cur_list in prefix_list:
|
||||
# we should exclude e.g. block.1 and block.12.attn
|
||||
if name.startswith(cur_prefix + "."):
|
||||
cur_list.append((name, submodule))
|
||||
break
|
||||
else:
|
||||
none_list.append((name, submodule))
|
||||
return prefix_list + [(None, none_list)] # type: ignore
|
||||
|
||||
|
||||
class LoRAModelLayers:
|
||||
|
||||
def __init__(self, block_list: list[tuple[str, nn.Module]]) -> None:
|
||||
# block_name -> {layer_name -> layer}
|
||||
self.block_to_lora_layers: dict[str, dict[str, BaseLayerWithLoRA]] = {}
|
||||
# layer_name -> block_name
|
||||
self.lora_layers_to_block: dict[str, str | None] = {}
|
||||
self.other_lora_layers: dict[str, BaseLayerWithLoRA] = {}
|
||||
self.block_mapping = dict(block_list)
|
||||
|
||||
def add_lora_layer(self, block_name: str | None, layer_name: str,
|
||||
layer: BaseLayerWithLoRA):
|
||||
if block_name is None:
|
||||
self.other_lora_layers[layer_name] = layer
|
||||
self.lora_layers_to_block[layer_name] = None
|
||||
else:
|
||||
if block_name not in self.block_to_lora_layers:
|
||||
self.block_to_lora_layers[block_name] = {}
|
||||
self.block_to_lora_layers[block_name][layer_name] = layer
|
||||
self.lora_layers_to_block[layer_name] = block_name
|
||||
|
||||
def all_lora_layers(
|
||||
self, ) -> Generator[tuple[str, BaseLayerWithLoRA], Any, None]:
|
||||
for block_layers in self.block_to_lora_layers.values():
|
||||
for name, layer in block_layers.items():
|
||||
yield name, layer
|
||||
for name, layer in self.other_lora_layers.items():
|
||||
yield name, layer
|
||||
|
||||
def lora_layers_by_block(
|
||||
self,
|
||||
) -> Generator[
|
||||
tuple[nn.Module | None, dict[str, BaseLayerWithLoRA]],
|
||||
Any,
|
||||
None,
|
||||
]:
|
||||
for block_name, layers in self.block_to_lora_layers.items():
|
||||
yield self.block_mapping[block_name], layers
|
||||
yield None, self.other_lora_layers
|
||||
|
||||
|
||||
class LoRAPipeline(ComposedPipelineBase):
|
||||
"""
|
||||
Pipeline that supports injecting LoRA adapters into the diffusion transformer.
|
||||
TODO: support training.
|
||||
"""
|
||||
|
||||
lora_adapters: dict[str, dict[str, torch.Tensor]] = defaultdict(
|
||||
dict
|
||||
) # state dicts of loaded lora adapters (includes lora_A, lora_B, and lora_alpha)
|
||||
cur_adapter_name: str = ""
|
||||
cur_adapter_path: str = ""
|
||||
# model_name -> layers
|
||||
lora_layers: dict[str, LoRAModelLayers] = {}
|
||||
lora_layers: dict[str, dict[str, BaseLayerWithLoRA]] = {}
|
||||
fastvideo_args: FastVideoArgs | TrainingArgs
|
||||
exclude_lora_layers: dict[str, list[str]] = {}
|
||||
device: torch.device = get_local_torch_device()
|
||||
@@ -127,10 +48,10 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
self.device = get_local_torch_device()
|
||||
# build list of trainable transformers
|
||||
for transformer_name in self.trainable_transformer_names:
|
||||
if (transformer_name in self.modules
|
||||
and self.modules[transformer_name] is not None):
|
||||
self.trainable_transformer_modules[transformer_name] = (
|
||||
self.modules[transformer_name])
|
||||
if transformer_name in self.modules and self.modules[
|
||||
transformer_name] is not None:
|
||||
self.trainable_transformer_modules[
|
||||
transformer_name] = self.modules[transformer_name]
|
||||
# check for transformer_2 in case of Wan2.2 MoE or fake_score_transformer_2
|
||||
if transformer_name.endswith("_2"):
|
||||
raise ValueError(
|
||||
@@ -138,23 +59,19 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
)
|
||||
|
||||
secondary_transformer_name = transformer_name + "_2"
|
||||
if (secondary_transformer_name in self.modules
|
||||
and self.modules[secondary_transformer_name] is not None):
|
||||
if secondary_transformer_name in self.modules and self.modules[
|
||||
secondary_transformer_name] is not None:
|
||||
self.trainable_transformer_modules[
|
||||
secondary_transformer_name] = self.modules[
|
||||
secondary_transformer_name]
|
||||
|
||||
logger.info(
|
||||
"trainable_transformer_modules: %s",
|
||||
self.trainable_transformer_modules.keys(),
|
||||
)
|
||||
logger.info("trainable_transformer_modules: %s",
|
||||
self.trainable_transformer_modules.keys())
|
||||
|
||||
for (
|
||||
transformer_name,
|
||||
transformer_module,
|
||||
) in self.trainable_transformer_modules.items():
|
||||
self.exclude_lora_layers[transformer_name] = (
|
||||
transformer_module.config.arch_config.exclude_lora_layers)
|
||||
for transformer_name, transformer_module in self.trainable_transformer_modules.items(
|
||||
):
|
||||
self.exclude_lora_layers[
|
||||
transformer_name] = transformer_module.config.arch_config.exclude_lora_layers
|
||||
self.lora_target_modules = self.fastvideo_args.lora_target_modules
|
||||
self.lora_path = self.fastvideo_args.lora_path
|
||||
self.lora_nickname = self.fastvideo_args.lora_nickname
|
||||
@@ -166,33 +83,20 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
self.fastvideo_args.lora_alpha = self.fastvideo_args.lora_rank
|
||||
self.lora_rank = self.fastvideo_args.lora_rank # type: ignore
|
||||
self.lora_alpha = self.fastvideo_args.lora_alpha # type: ignore
|
||||
logger.info(
|
||||
"Using LoRA training with rank %d and alpha %d",
|
||||
self.lora_rank,
|
||||
self.lora_alpha,
|
||||
)
|
||||
logger.info("Using LoRA training with rank %d and alpha %d",
|
||||
self.lora_rank, self.lora_alpha)
|
||||
if self.lora_target_modules is None:
|
||||
self.lora_target_modules = [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
"o_proj",
|
||||
"to_q",
|
||||
"to_k",
|
||||
"to_v",
|
||||
"to_out",
|
||||
"to_qkv",
|
||||
"to_gate_compress",
|
||||
"q_proj", "k_proj", "v_proj", "o_proj", "to_q", "to_k",
|
||||
"to_v", "to_out", "to_qkv", "to_gate_compress"
|
||||
]
|
||||
logger.info(
|
||||
"Using default lora_target_modules for all transformers: %s",
|
||||
self.lora_target_modules,
|
||||
)
|
||||
self.lora_target_modules)
|
||||
else:
|
||||
logger.warning(
|
||||
"Using custom lora_target_modules for all transformers, which may not be intended: %s",
|
||||
self.lora_target_modules,
|
||||
)
|
||||
self.lora_target_modules)
|
||||
|
||||
self.convert_to_lora_layers()
|
||||
# Inference
|
||||
@@ -200,8 +104,7 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
self.convert_to_lora_layers()
|
||||
self.set_lora_adapter(
|
||||
self.lora_nickname, # type: ignore
|
||||
self.lora_path,
|
||||
) # type: ignore
|
||||
self.lora_path) # type: ignore
|
||||
|
||||
def is_target_layer(self, module_name: str) -> bool:
|
||||
if self.lora_target_modules is None:
|
||||
@@ -211,9 +114,9 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
|
||||
def set_trainable(self) -> None:
|
||||
|
||||
def set_lora_grads(lora_layers: LoRAModelLayers,
|
||||
def set_lora_grads(lora_layers: dict[str, BaseLayerWithLoRA],
|
||||
device_mesh: DeviceMesh):
|
||||
for name, layer in lora_layers.all_lora_layers():
|
||||
for name, layer in lora_layers.items():
|
||||
layer.lora_A.requires_grad_(True)
|
||||
layer.lora_B.requires_grad_(True)
|
||||
layer.base_layer.requires_grad_(False)
|
||||
@@ -228,15 +131,10 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
super().set_trainable()
|
||||
return
|
||||
|
||||
device_mesh = init_device_mesh(
|
||||
"cuda",
|
||||
(dist.get_world_size(), 1),
|
||||
mesh_dim_names=["fake", "replicate"],
|
||||
)
|
||||
for (
|
||||
transformer_name,
|
||||
transformer_module,
|
||||
) in self.trainable_transformer_modules.items():
|
||||
device_mesh = init_device_mesh("cuda", (dist.get_world_size(), 1),
|
||||
mesh_dim_names=["fake", "replicate"])
|
||||
for transformer_name, transformer_module in self.trainable_transformer_modules.items(
|
||||
):
|
||||
transformer_module.train()
|
||||
transformer_module.requires_grad_(False)
|
||||
if transformer_name in self.lora_layers:
|
||||
@@ -253,71 +151,32 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
if self.lora_initialized:
|
||||
return
|
||||
self.lora_initialized = True
|
||||
for (
|
||||
transformer_name,
|
||||
transformer_module,
|
||||
) in self.trainable_transformer_modules.items():
|
||||
for transformer_name, transformer_module in self.trainable_transformer_modules.items(
|
||||
):
|
||||
converted_count = 0
|
||||
# init bookkeeping structures
|
||||
if transformer_name not in self.lora_layers:
|
||||
# get block list
|
||||
block_list = []
|
||||
for name, submodule in transformer_module.named_children():
|
||||
if isinstance(submodule, nn.ModuleList):
|
||||
block_list = [(f"{name}.{i}", m)
|
||||
for i, m in enumerate(submodule)]
|
||||
break
|
||||
self.lora_layers[transformer_name] = LoRAModelLayers(block_list)
|
||||
self.lora_layers[transformer_name] = {}
|
||||
logger.info("Converting %s to LoRA Transformer", transformer_name)
|
||||
# scan every module and convert to LoRA layer if applicable
|
||||
for name, layer in transformer_module.named_modules():
|
||||
if not self.is_target_layer(name):
|
||||
continue
|
||||
|
||||
for block_name, block_modules in _named_module_by_prefix(
|
||||
transformer_module,
|
||||
list(self.lora_layers[transformer_name].block_mapping),
|
||||
):
|
||||
if block_name is not None and (
|
||||
not self.fastvideo_args.training_mode
|
||||
and self.fastvideo_args.dit_layerwise_offload):
|
||||
scope_ctx = _get_hook_ctx(
|
||||
self.lora_layers[transformer_name].
|
||||
block_mapping[block_name])
|
||||
else:
|
||||
scope_ctx = nullcontext()
|
||||
with scope_ctx:
|
||||
for name, layer in block_modules:
|
||||
if not self.is_target_layer(name):
|
||||
continue
|
||||
excluded = False
|
||||
for exclude_layer in self.exclude_lora_layers[transformer_name]:
|
||||
if exclude_layer in name:
|
||||
excluded = True
|
||||
break
|
||||
if excluded:
|
||||
continue
|
||||
|
||||
excluded = False
|
||||
for exclude_layer in self.exclude_lora_layers[
|
||||
transformer_name]:
|
||||
if exclude_layer in name:
|
||||
excluded = True
|
||||
break
|
||||
if excluded:
|
||||
continue
|
||||
|
||||
layer = get_lora_layer(
|
||||
layer,
|
||||
lora_rank=self.lora_rank,
|
||||
lora_alpha=self.lora_alpha,
|
||||
training_mode=self.training_mode,
|
||||
)
|
||||
if layer is not None:
|
||||
block_name_split = name.split(".", 2)
|
||||
if len(block_name_split) > 2:
|
||||
block_name = (block_name_split[0] + "." +
|
||||
block_name_split[1])
|
||||
else:
|
||||
block_name = None
|
||||
if (block_name
|
||||
not in self.lora_layers[transformer_name].
|
||||
block_mapping):
|
||||
block_name = None
|
||||
self.lora_layers[transformer_name].add_lora_layer(
|
||||
block_name, name, layer)
|
||||
replace_submodule(transformer_module, name, layer)
|
||||
converted_count += 1
|
||||
layer = get_lora_layer(layer,
|
||||
lora_rank=self.lora_rank,
|
||||
lora_alpha=self.lora_alpha,
|
||||
training_mode=self.training_mode)
|
||||
if layer is not None:
|
||||
self.lora_layers[transformer_name][name] = layer
|
||||
replace_submodule(transformer_module, name, layer)
|
||||
converted_count += 1
|
||||
logger.info("Converted %d layers to LoRA layers", converted_count)
|
||||
|
||||
def set_lora_adapter(self,
|
||||
@@ -350,7 +209,7 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
|
||||
# Extract alpha values and weights in a single pass
|
||||
to_merge_params: defaultdict[Hashable,
|
||||
dict[Any, Any]] = (defaultdict(dict))
|
||||
dict[Any, Any]] = defaultdict(dict)
|
||||
for name, weight in lora_state_dict.items():
|
||||
# Extract weights (lora_A, lora_B, and lora_alpha)
|
||||
name = name.replace("diffusion_model.", "")
|
||||
@@ -364,14 +223,13 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
target_name, _, _ = param_names_mapping_fn(layer_name)
|
||||
# Store alpha alongside weights with same target_name base
|
||||
alpha_key = target_name + ".lora_alpha"
|
||||
self.lora_adapters[lora_nickname][alpha_key] = (
|
||||
weight.item()
|
||||
if weight.numel() == 1 else float(weight.mean()))
|
||||
self.lora_adapters[lora_nickname][alpha_key] = weight.item(
|
||||
) if weight.numel() == 1 else float(weight.mean())
|
||||
continue
|
||||
|
||||
name, _, _ = lora_param_names_mapping_fn(name)
|
||||
target_name, merge_index, num_params_to_merge = (
|
||||
param_names_mapping_fn(name))
|
||||
target_name, merge_index, num_params_to_merge = param_names_mapping_fn(
|
||||
name)
|
||||
# for (in_dim, r) @ (r, out_dim), we only merge (r, out_dim * n) where n is the number of linear layers to fuse
|
||||
# see param mapping in HunyuanVideoArchConfig
|
||||
if merge_index is not None and "lora_B" in name:
|
||||
@@ -403,84 +261,45 @@ class LoRAPipeline(ComposedPipelineBase):
|
||||
|
||||
# Merge the new adapter
|
||||
adapted_count = 0
|
||||
for (
|
||||
transformer_name,
|
||||
transformer_lora_layers,
|
||||
) in self.lora_layers.items():
|
||||
for (
|
||||
module,
|
||||
layers,
|
||||
) in transformer_lora_layers.lora_layers_by_block():
|
||||
with _get_hook_ctx(module):
|
||||
for name, layer in layers.items():
|
||||
lora_A_name = name + ".lora_A"
|
||||
lora_B_name = name + ".lora_B"
|
||||
lora_alpha_name = name + ".lora_alpha"
|
||||
if (lora_A_name in self.lora_adapters[lora_nickname]
|
||||
and lora_B_name
|
||||
in self.lora_adapters[lora_nickname]):
|
||||
# Get alpha value for this layer (defaults to None if not present)
|
||||
lora_A = self.lora_adapters[lora_nickname][
|
||||
lora_A_name]
|
||||
lora_B = self.lora_adapters[lora_nickname][
|
||||
lora_B_name]
|
||||
# Simple lookup - alpha stored with same naming scheme as lora_A/lora_B
|
||||
alpha = (self.lora_adapters[lora_nickname].get(
|
||||
lora_alpha_name) if adapter_updated else None)
|
||||
try:
|
||||
layer.set_lora_weights(
|
||||
lora_A,
|
||||
lora_B,
|
||||
lora_alpha=alpha,
|
||||
training_mode=self.fastvideo_args.
|
||||
training_mode,
|
||||
lora_path=lora_path,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Error setting LoRA weights for layer %s: %s",
|
||||
name,
|
||||
str(e),
|
||||
)
|
||||
raise e
|
||||
adapted_count += 1
|
||||
else:
|
||||
if rank == 0:
|
||||
logger.warning(
|
||||
"LoRA adapter %s does not contain the weights for layer %s. LoRA will not be applied to it.",
|
||||
lora_path,
|
||||
name,
|
||||
)
|
||||
layer.disable_lora = True
|
||||
logger.info(
|
||||
"Rank %d: LoRA adapter %s applied to %d layers",
|
||||
rank,
|
||||
lora_path,
|
||||
adapted_count,
|
||||
)
|
||||
for transformer_name, transformer_lora_layers in self.lora_layers.items(
|
||||
):
|
||||
for name, layer in transformer_lora_layers.items():
|
||||
lora_A_name = name + ".lora_A"
|
||||
lora_B_name = name + ".lora_B"
|
||||
lora_alpha_name = name + ".lora_alpha"
|
||||
if lora_A_name in self.lora_adapters[lora_nickname]\
|
||||
and lora_B_name in self.lora_adapters[lora_nickname]:
|
||||
# Get alpha value for this layer (defaults to None if not present)
|
||||
lora_A = self.lora_adapters[lora_nickname][lora_A_name]
|
||||
lora_B = self.lora_adapters[lora_nickname][lora_B_name]
|
||||
# Simple lookup - alpha stored with same naming scheme as lora_A/lora_B
|
||||
alpha = self.lora_adapters[lora_nickname].get(
|
||||
lora_alpha_name) if adapter_updated else None
|
||||
|
||||
layer.set_lora_weights(
|
||||
lora_A,
|
||||
lora_B,
|
||||
lora_alpha=alpha,
|
||||
training_mode=self.fastvideo_args.training_mode,
|
||||
lora_path=lora_path)
|
||||
adapted_count += 1
|
||||
else:
|
||||
if rank == 0:
|
||||
logger.warning(
|
||||
"LoRA adapter %s does not contain the weights for layer %s. LoRA will not be applied to it.",
|
||||
lora_path, name)
|
||||
layer.disable_lora = True
|
||||
logger.info("Rank %d: LoRA adapter %s applied to %d layers", rank,
|
||||
lora_path, adapted_count)
|
||||
|
||||
def merge_lora_weights(self) -> None:
|
||||
for (
|
||||
transformer_name,
|
||||
transformer_lora_layers,
|
||||
) in self.lora_layers.items():
|
||||
for (
|
||||
module,
|
||||
layers,
|
||||
) in transformer_lora_layers.lora_layers_by_block():
|
||||
with _get_hook_ctx(module):
|
||||
for name, layer in layers.items():
|
||||
layer.merge_lora_weights()
|
||||
for transformer_name, transformer_lora_layers in self.lora_layers.items(
|
||||
):
|
||||
for name, layer in transformer_lora_layers.items():
|
||||
layer.merge_lora_weights()
|
||||
|
||||
def unmerge_lora_weights(self) -> None:
|
||||
for (
|
||||
transformer_name,
|
||||
transformer_lora_layers,
|
||||
) in self.lora_layers.items():
|
||||
for (
|
||||
module,
|
||||
layers,
|
||||
) in transformer_lora_layers.lora_layers_by_block():
|
||||
with _get_hook_ctx(module):
|
||||
for name, layer in layers.items():
|
||||
layer.unmerge_lora_weights()
|
||||
for transformer_name, transformer_lora_layers in self.lora_layers.items(
|
||||
):
|
||||
for name, layer in transformer_lora_layers.items():
|
||||
layer.unmerge_lora_weights()
|
||||
|
||||
@@ -24,18 +24,13 @@ _PIPELINE_NAME_TO_ARCHITECTURE_NAME: dict[str, str] = {
|
||||
"WanVideoToVideoPipeline": "wan",
|
||||
"WanCausalDMDPipeline": "wan",
|
||||
"TurboDiffusionPipeline": "turbodiffusion",
|
||||
"TurboDiffusionI2VPipeline": "turbodiffusion",
|
||||
"StepVideoPipeline": "stepvideo",
|
||||
"HunyuanVideoPipeline": "hunyuan",
|
||||
"HunyuanVideo15Pipeline": "hunyuan15",
|
||||
"Cosmos2VideoToWorldPipeline": "cosmos",
|
||||
"Cosmos2_5Pipeline": "cosmos",
|
||||
"MatrixGamePipeline": "matrixgame",
|
||||
"MatrixGameCausalDMDPipeline": "matrixgame",
|
||||
"LongCatPipeline": "longcat",
|
||||
"LongCatImageToVideoPipeline": "longcat",
|
||||
"LongCatVideoContinuationPipeline": "longcat",
|
||||
"LTX2Pipeline": "ltx2",
|
||||
}
|
||||
|
||||
_PREPROCESS_WORKLOAD_TYPE_TO_PIPELINE_NAME: dict[WorkloadType, str] = {
|
||||
|
||||
@@ -10,8 +10,7 @@ from fastvideo.pipelines.stages.base import PipelineStage
|
||||
from fastvideo.pipelines.stages.causal_denoising import CausalDMDDenosingStage
|
||||
from fastvideo.pipelines.stages.conditioning import ConditioningStage
|
||||
from fastvideo.pipelines.stages.decoding import DecodingStage
|
||||
from fastvideo.pipelines.stages.denoising import (Cosmos25DenoisingStage,
|
||||
CosmosDenoisingStage,
|
||||
from fastvideo.pipelines.stages.denoising import (CosmosDenoisingStage,
|
||||
DenoisingStage,
|
||||
DmdDenoisingStage)
|
||||
from fastvideo.pipelines.stages.encoding import EncodingStage
|
||||
@@ -20,44 +19,27 @@ from fastvideo.pipelines.stages.image_encoding import (
|
||||
ImageVAEEncodingStage, VideoVAEEncodingStage, Hy15ImageEncodingStage)
|
||||
from fastvideo.pipelines.stages.input_validation import InputValidationStage
|
||||
from fastvideo.pipelines.stages.latent_preparation import (
|
||||
Cosmos25LatentPreparationStage, CosmosLatentPreparationStage,
|
||||
LatentPreparationStage)
|
||||
from fastvideo.pipelines.stages.ltx2_audio_decoding import LTX2AudioDecodingStage
|
||||
from fastvideo.pipelines.stages.ltx2_denoising import LTX2DenoisingStage
|
||||
from fastvideo.pipelines.stages.ltx2_latent_preparation import (
|
||||
LTX2LatentPreparationStage)
|
||||
CosmosLatentPreparationStage, LatentPreparationStage)
|
||||
from fastvideo.pipelines.stages.matrixgame_denoising import (
|
||||
MatrixGameCausalDenoisingStage)
|
||||
from fastvideo.pipelines.stages.stepvideo_encoding import (
|
||||
StepvideoPromptEncodingStage)
|
||||
from fastvideo.pipelines.stages.text_encoding import (Cosmos25TextEncodingStage,
|
||||
TextEncodingStage)
|
||||
from fastvideo.pipelines.stages.text_encoding import TextEncodingStage
|
||||
from fastvideo.pipelines.stages.timestep_preparation import (
|
||||
Cosmos25TimestepPreparationStage, TimestepPreparationStage)
|
||||
|
||||
# LongCat stages
|
||||
from fastvideo.pipelines.stages.longcat_video_vae_encoding import LongCatVideoVAEEncodingStage
|
||||
from fastvideo.pipelines.stages.longcat_kv_cache_init import LongCatKVCacheInitStage
|
||||
from fastvideo.pipelines.stages.longcat_vc_denoising import LongCatVCDenoisingStage
|
||||
TimestepPreparationStage)
|
||||
|
||||
__all__ = [
|
||||
"PipelineStage",
|
||||
"InputValidationStage",
|
||||
"TimestepPreparationStage",
|
||||
"Cosmos25TimestepPreparationStage",
|
||||
"LatentPreparationStage",
|
||||
"CosmosLatentPreparationStage",
|
||||
"Cosmos25LatentPreparationStage",
|
||||
"LTX2LatentPreparationStage",
|
||||
"LTX2AudioDecodingStage",
|
||||
"ConditioningStage",
|
||||
"DenoisingStage",
|
||||
"DmdDenoisingStage",
|
||||
"CausalDMDDenosingStage",
|
||||
"MatrixGameCausalDenoisingStage",
|
||||
"CosmosDenoisingStage",
|
||||
"Cosmos25DenoisingStage",
|
||||
"LTX2DenoisingStage",
|
||||
"EncodingStage",
|
||||
"DecodingStage",
|
||||
"ImageEncodingStage",
|
||||
@@ -67,10 +49,5 @@ __all__ = [
|
||||
"ImageVAEEncodingStage",
|
||||
"VideoVAEEncodingStage",
|
||||
"TextEncodingStage",
|
||||
"Cosmos25TextEncodingStage",
|
||||
"StepvideoPromptEncodingStage",
|
||||
# LongCat stages
|
||||
"LongCatVideoVAEEncodingStage",
|
||||
"LongCatKVCacheInitStage",
|
||||
"LongCatVCDenoisingStage",
|
||||
]
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user