diff --git a/LICENSE b/LICENSE index 333363536..a7b8b5e6a 100644 --- a/LICENSE +++ b/LICENSE @@ -184,18 +184,4 @@ comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [2023] Lightning AI - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. \ No newline at end of file + identification within third-party archives. \ No newline at end of file diff --git a/README.md b/README.md index 3202ce8ef..19740fdfa 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ https://github.com/user-attachments/assets/79af5fb8-707c-4263-b153-9ab2a01d3ac1 FastVideo currently offers: (with more to come) -- [NEW!] [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/) that reduce HunyuanVideo's inference cost from 16 minutes to 5 minutes. +- [NEW!] [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/). - FastHunyuan and FastMochi: consistency distilled video diffusion models for 8x inference speedup. - First open distillation recipes for video DiT, based on [PCM](https://github.com/G-U-N/Phased-Consistency-Model). - Support distilling/finetuning/inferencing state-of-the-art open video DiTs: 1. Mochi 2. Hunyuan. @@ -31,7 +31,8 @@ Dev in progress and highly experimental. ## Change Log -- ```2025/02/14```: Release the inference code and kernel for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/). +- ```2025/02/20```: FastVideo now supports STA on [StepVideo](https://github.com/stepfun-ai/Step-Video-T2V) with 3.4X speedup! +- ```2025/02/18```: Release the inference code and kernel for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/). - ```2025/01/13```: Support Lora finetuning for HunyuanVideo. - ```2024/12/25```: Enable single 4090 inference for `FastHunyuan`, please rerun the installation steps to update the environment. - ```2024/12/17```: `FastVideo` v1.0 is released. @@ -45,7 +46,13 @@ The code is tested on Python 3.10.0, CUDA 12.4 and H100. To try Sliding Tile Attention (optional), please follow the instruction in [csrc/sliding_tile_attention/README.md](csrc/sliding_tile_attention/README.md) to install STA. ## 🚀 Inference - +### Inference StepVideo with Sliding Tile Attention +```bash +python scripts/huggingface/download_hf.py --repo_id=stepfun-ai/stepvideo-t2v --local_dir=data/stepvideo-t2v --repo_type=model +sh scripts/inference/inference_stepvideo_STA.sh # Inference stepvideo with STA +sh scripts/inference/inference_stepvideo.sh # Inference original stepvideo +``` +When using STA for inference, the generated videos will have dimensions of 204×768×768 (currently, this is the only supported shape). ### Inference HunyuanVideo with Sliding Tile Attention First, download the model: ``` diff --git a/assets/mask_strategy.json b/assets/mask_strategy_hunyuan.json similarity index 100% rename from assets/mask_strategy.json rename to assets/mask_strategy_hunyuan.json diff --git a/assets/mask_strategy_stepvideo.json 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--git a/csrc/sliding_tile_attention/README.md b/csrc/sliding_tile_attention/README.md index d4fcbf2e3..44a161c43 100644 --- a/csrc/sliding_tile_attention/README.md +++ b/csrc/sliding_tile_attention/README.md @@ -7,12 +7,24 @@ ## Installation -We test our code on Pytorch 2.5.1 and CUDA 12.4. Currently we only have implementation on H100. +We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only have implementation on H100. +First, install C++20 for ThunderKittens: + +```bash +sudo apt update +sudo apt install gcc-11 g++-11 + +sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11 + +sudo apt update +sudo apt install clang-11 +``` +Install STA: ```bash export CUDA_HOME=/usr/local/cuda-12.4 export PATH=${CUDA_HOME}/bin:${PATH} export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH -git pull --recurse-submodules +git submodule update --init --recursive python setup.py install ``` diff --git a/csrc/sliding_tile_attention/config.py b/csrc/sliding_tile_attention/config.py index a5523ae48..995aa013c 100644 --- a/csrc/sliding_tile_attention/config.py +++ b/csrc/sliding_tile_attention/config.py @@ -2,8 +2,7 @@ sources = { 'attn': { 'source_files': { - 'h100': - 'st_attn/st_attn_h100.cu' # define these source files for each GPU target desired. + 'h100': 'st_attn/st_attn_h100.cu' # define these source files for each GPU target desired. } } } diff --git a/csrc/sliding_tile_attention/setup.py b/csrc/sliding_tile_attention/setup.py index b78804265..4b5987012 100644 --- a/csrc/sliding_tile_attention/setup.py +++ b/csrc/sliding_tile_attention/setup.py @@ -8,11 +8,9 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension target = target.lower() # Set environment variables -tk_root = os.getenv('THUNDERKITTENS_ROOT', - os.path.abspath(os.path.join(os.getcwd(), 'tk/'))) -python_include = subprocess.check_output([ - 'python', '-c', "import sysconfig; print(sysconfig.get_path('include'))" -]).decode().strip() +tk_root = os.getenv('THUNDERKITTENS_ROOT', os.path.abspath(os.path.join(os.getcwd(), 'tk/'))) +python_include = subprocess.check_output(['python', '-c', + "import sysconfig; print(sysconfig.get_path('include'))"]).decode().strip() torch_include = subprocess.check_output([ 'python', '-c', "import torch; from torch.utils.cpp_extension import include_paths; print(' '.join(['-I' + p for p in include_paths()]))" @@ -23,11 +21,9 @@ print('Torch include directories:', torch_include) # CUDA flags cuda_flags = [ - '-DNDEBUG', '-Xcompiler=-Wno-psabi', '-Xcompiler=-fno-strict-aliasing', - '--expt-extended-lambda', '--expt-relaxed-constexpr', - '-forward-unknown-to-host-compiler', '--use_fast_math', '-std=c++20', - '-O3', '-Xnvlink=--verbose', '-Xptxas=--verbose', - '-Xptxas=--warn-on-spills', f'-I{tk_root}/include', + '-DNDEBUG', '-Xcompiler=-Wno-psabi', '-Xcompiler=-fno-strict-aliasing', '--expt-extended-lambda', + '--expt-relaxed-constexpr', '-forward-unknown-to-host-compiler', '--use_fast_math', '-std=c++20', '-O3', + '-Xnvlink=--verbose', '-Xptxas=--verbose', '-Xptxas=--warn-on-spills', f'-I{tk_root}/include', f'-I{tk_root}/prototype', f'-I{python_include}', '-DTORCH_COMPILE' ] + torch_include.split() cpp_flags = ['-std=c++20', '-O3'] @@ -41,8 +37,7 @@ else: source_files = ['st_attn.cpp'] for k in kernels: if target not in sources[k]['source_files']: - raise KeyError( - f'Target {target} not found in source files for kernel {k}') + raise KeyError(f'Target {target} not found in source files for kernel {k}') if isinstance(sources[k]['source_files'][target], list): source_files.extend(sources[k]['source_files'][target]) else: diff --git a/csrc/sliding_tile_attention/st_attn.cpp b/csrc/sliding_tile_attention/st_attn.cpp index 7d108ff11..c3632e834 100644 --- a/csrc/sliding_tile_attention/st_attn.cpp +++ b/csrc/sliding_tile_attention/st_attn.cpp @@ -10,7 +10,7 @@ #ifdef TK_COMPILE_ATTN extern torch::Tensor sta_forward( - torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o, int kernel_t_size, int kernel_w_size, int kernel_h_size, int text_length, bool process_text + torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o, int kernel_t_size, int kernel_w_size, int kernel_h_size, int text_length, bool process_text, bool has_text ); #endif diff --git a/csrc/sliding_tile_attention/st_attn/__init__.py b/csrc/sliding_tile_attention/st_attn/__init__.py index aba12afe2..195acc976 100644 --- a/csrc/sliding_tile_attention/st_attn/__init__.py +++ b/csrc/sliding_tile_attention/st_attn/__init__.py @@ -4,34 +4,32 @@ import torch from st_attn_cuda import sta_fwd -def sliding_tile_attention(q_all, k_all, v_all, window_size, text_length): +def sliding_tile_attention(q_all, k_all, v_all, window_size, text_length, has_text=True): seq_length = q_all.shape[2] - assert q_all.shape[ - 2] == 115456, "STA currently only supports video with latent size (30, 48, 80), which is 117 frames x 768 x 1280 pixels" - assert q_all.shape[1] == len( - window_size), "Number of heads must match the number of window sizes" - target_size = math.ceil(seq_length / 384) * 384 - pad_size = target_size - seq_length - if pad_size > 0: - q_all = torch.cat([q_all, q_all[:, :, -pad_size:]], dim=2) - k_all = torch.cat([k_all, k_all[:, :, -pad_size:]], dim=2) - v_all = torch.cat([v_all, v_all[:, :, -pad_size:]], dim=2) + if has_text: + assert q_all.shape[ + 2] == 115456, "STA currently only supports video with latent size (30, 48, 80), which is 117 frames x 768 x 1280 pixels" + assert q_all.shape[1] == len(window_size), "Number of heads must match the number of window sizes" + target_size = math.ceil(seq_length / 384) * 384 + pad_size = target_size - seq_length + if pad_size > 0: + q_all = torch.cat([q_all, q_all[:, :, -pad_size:]], dim=2) + k_all = torch.cat([k_all, k_all[:, :, -pad_size:]], dim=2) + v_all = torch.cat([v_all, v_all[:, :, -pad_size:]], dim=2) + else: + assert q_all.shape[2] == 82944 hidden_states = torch.empty_like(q_all) + # This for loop is ugly. but it is actually quite efficient. The sequence dimension alone can already oversubscribe SMs for head_index, (t_kernel, h_kernel, w_kernel) in enumerate(window_size): - q_head, k_head, v_head, o_head = q_all[:, head_index:head_index + - 1], k_all[:, head_index: - head_index + - 1], v_all[:, - head_index: - head_index + - 1], hidden_states[:, - head_index: - head_index - + - 1] - _ = sta_fwd(q_head, k_head, v_head, o_head, t_kernel, h_kernel, - w_kernel, text_length, False) + for batch in range(q_all.shape[0]): + q_head, k_head, v_head, o_head = (q_all[batch:batch + 1, head_index:head_index + 1], + k_all[batch:batch + 1, + head_index:head_index + 1], v_all[batch:batch + 1, + head_index:head_index + 1], + hidden_states[batch:batch + 1, head_index:head_index + 1]) - _ = sta_fwd(q_all, k_all, v_all, hidden_states, 3, 3, 3, text_length, True) + _ = sta_fwd(q_head, k_head, v_head, o_head, t_kernel, h_kernel, w_kernel, text_length, False, has_text) + if has_text: + _ = sta_fwd(q_all, k_all, v_all, hidden_states, 3, 3, 3, text_length, True) return hidden_states[:, :, :seq_length] diff --git a/csrc/sliding_tile_attention/st_attn/st_attn_h100.cu b/csrc/sliding_tile_attention/st_attn/st_attn_h100.cu index 6cd633ee7..e0e8c4091 100644 --- a/csrc/sliding_tile_attention/st_attn/st_attn_h100.cu +++ b/csrc/sliding_tile_attention/st_attn/st_attn_h100.cu @@ -55,7 +55,7 @@ template struct fwd_globals { }; -template +template __global__ __launch_bounds__((NUM_WORKERS)*kittens::WARP_THREADS, 1) void fwd_attend_ker(const __grid_constant__ fwd_globals g) { extern __shared__ int __shm[]; @@ -75,8 +75,13 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals g) { v_tile (&v_smem)[K::stages] = al.allocate(); l_col_vec (&l_smem)[CONSUMER_WARPGROUPS] = al.allocate(); auto (*o_smem) = reinterpret_cast(q_smem); - + int img_kv_blocks; int kv_blocks = g.N / (K::kv_height); + if constexpr (text_kv) { + img_kv_blocks = kv_blocks - 3; + } else { + img_kv_blocks = kv_blocks; + } int kv_head_idx = blockIdx.y / g.hr; int seq_idx; if constexpr (text_q) { @@ -193,7 +198,7 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals g) { } } // for text - for (int index = kv_blocks - 3; index < kv_blocks; index++) { + for (int index = img_kv_blocks; index < kv_blocks; index++) { coord kv_tile_idx = {blockIdx.z, kv_head_idx, index, 0}; tma::expect_bytes(k_smem_arrived[count%K::stages], sizeof(k_tile)); tma::load_async(k_smem[count%K::stages], g.k, kv_tile_idx, k_smem_arrived[count%K::stages]); @@ -227,7 +232,7 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals g) { } else if constexpr (text_q){ // the last three kv blocks are for text, we process them separately - kv_iters = kv_blocks - 1 - 3; + kv_iters = img_kv_blocks - 1; } else { kv_iters = CLAMP(DT*2+1, 1, CT) * CLAMP(DH*2+1, 1, CH) * CLAMP(DW*2+1, 1, CW) * 3 - 1 ; } @@ -273,49 +278,51 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals g) { if(warpgroup::laneid() == 0) arrive(compute_done[(kv_idx)%K::stages], 1); } // the last three kv blocks are for text, we process them separately - for (auto kv_idx = kv_iters + 1; kv_idx <= kv_iters + 3; kv_idx++) { + if constexpr(text_kv) { + for (auto kv_idx = kv_iters + 1; kv_idx <= kv_iters + 3; kv_idx++) { - wait(k_smem_arrived[(kv_idx)%K::stages], (kv_idx/K::stages)%2); - warpgroup::mm_ABt(att_block, q_smem[warpgroupid], k_smem[(kv_idx)%K::stages]); - - copy(max_vec_last_scaled, max_vec); - if constexpr (D == 64) { mul(max_vec_last_scaled, max_vec_last_scaled, 1.44269504089f*0.125f); } - else { mul(max_vec_last_scaled, max_vec_last_scaled, 1.44269504089f*0.08838834764f); } - - warpgroup::mma_async_wait(); - // apply non-pad mask - int offset = g.text_L - (kv_idx - (kv_iters + 1)) * K::kv_height; - // printf("k_idx_start: %d, k_idx_end: %d, text_end: %d, offset: %d\n", k_idx_start, k_idx_end, text_end, offset); - right_fill(att_block, att_block, offset, base_types::constants::neg_infty()); + wait(k_smem_arrived[(kv_idx)%K::stages], (kv_idx/K::stages)%2); + warpgroup::mm_ABt(att_block, q_smem[warpgroupid], k_smem[(kv_idx)%K::stages]); + + copy(max_vec_last_scaled, max_vec); + if constexpr (D == 64) { mul(max_vec_last_scaled, max_vec_last_scaled, 1.44269504089f*0.125f); } + else { mul(max_vec_last_scaled, max_vec_last_scaled, 1.44269504089f*0.08838834764f); } + + warpgroup::mma_async_wait(); + // apply non-pad mask + int offset = g.text_L - (kv_idx - (kv_iters + 1)) * K::kv_height; + // printf("k_idx_start: %d, k_idx_end: %d, text_end: %d, offset: %d\n", k_idx_start, k_idx_end, text_end, offset); + right_fill(att_block, att_block, offset, base_types::constants::neg_infty()); - row_max(max_vec, att_block, max_vec); - - if constexpr (D == 64) { - mul(att_block, att_block, 1.44269504089f*0.125f); - mul(max_vec_scaled, max_vec, 1.44269504089f*0.125f); + row_max(max_vec, att_block, max_vec); + + if constexpr (D == 64) { + mul(att_block, att_block, 1.44269504089f*0.125f); + mul(max_vec_scaled, max_vec, 1.44269504089f*0.125f); + } + else { + mul(att_block, att_block, 1.44269504089f*0.08838834764f); + mul(max_vec_scaled, max_vec, 1.44269504089f*0.08838834764f); + } + + sub_row(att_block, att_block, max_vec_scaled); + exp2(att_block, att_block); + sub(max_vec_last_scaled, max_vec_last_scaled, max_vec_scaled); + exp2(max_vec_last_scaled, max_vec_last_scaled); + mul(norm_vec, norm_vec, max_vec_last_scaled); + row_sum(norm_vec, att_block, norm_vec); + add(att_block, att_block, 0.f); + copy(att_block_mma, att_block); + mul_row(o_reg, o_reg, max_vec_last_scaled); + + wait(v_smem_arrived[(kv_idx)%K::stages], (kv_idx/K::stages)%2); + + warpgroup::mma_AB(o_reg, att_block_mma, v_smem[(kv_idx)%K::stages]); + warpgroup::mma_async_wait(); + + if(warpgroup::laneid() == 0) arrive(compute_done[(kv_idx)%K::stages], 1); } - else { - mul(att_block, att_block, 1.44269504089f*0.08838834764f); - mul(max_vec_scaled, max_vec, 1.44269504089f*0.08838834764f); - } - - sub_row(att_block, att_block, max_vec_scaled); - exp2(att_block, att_block); - sub(max_vec_last_scaled, max_vec_last_scaled, max_vec_scaled); - exp2(max_vec_last_scaled, max_vec_last_scaled); - mul(norm_vec, norm_vec, max_vec_last_scaled); - row_sum(norm_vec, att_block, norm_vec); - add(att_block, att_block, 0.f); - copy(att_block_mma, att_block); - mul_row(o_reg, o_reg, max_vec_last_scaled); - - wait(v_smem_arrived[(kv_idx)%K::stages], (kv_idx/K::stages)%2); - - warpgroup::mma_AB(o_reg, att_block_mma, v_smem[(kv_idx)%K::stages]); - warpgroup::mma_async_wait(); - - if(warpgroup::laneid() == 0) arrive(compute_done[(kv_idx)%K::stages], 1); } div_row(o_reg, o_reg, norm_vec); @@ -352,7 +359,7 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals g) { #include torch::Tensor -sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o, int kernel_t_size, int kernel_h_size, int kernel_w_size, int text_length, bool process_text) +sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o, int kernel_t_size, int kernel_h_size, int kernel_w_size, int text_length, bool process_text, bool has_text) { CHECK_INPUT(q); CHECK_INPUT(k); @@ -437,119 +444,232 @@ sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o, auto mem_size = kittens::MAX_SHARED_MEMORY; auto threads = NUM_WORKERS * kittens::WARP_THREADS; + if (has_text) { + // TORCH_CHECK(seq_len % (CONSUMER_WARPGROUPS*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 192"); + dim3 grid_image(seq_len/(CONSUMER_WARPGROUPS*kittens::TILE_ROW_DIM*4-2), qo_heads, batch); + dim3 grid_text(2, qo_heads, batch); + if (!process_text) { + if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 3) { - // TORCH_CHECK(seq_len % (CONSUMER_WARPGROUPS*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 192"); - dim3 grid_image(seq_len/(CONSUMER_WARPGROUPS*kittens::TILE_ROW_DIM*4-2), qo_heads, batch); - dim3 grid_text(2, qo_heads, batch); - if (!process_text) { - if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 3) { + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 1, 1, 1, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 1, 1, 1, 5, 6, 10><<>>(g); - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 1, 1, 1, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 1, 1, 1, 5, 6, 10><<>>(g); + } else if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 5) { + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 1, 1, 2, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true,1, 1, 2, 5, 6, 10><<>>(g); - } else if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 5) { - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 1, 1, 2, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 1, 1, 2, 5, 6, 10><<>>(g); + } else if (kernel_t_size == 5 && kernel_h_size == 3 && kernel_w_size == 3) { + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 2, 1, 1, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 2, 1, 1, 5, 6, 10><<>>(g); - } else if (kernel_t_size == 5 && kernel_h_size == 3 && kernel_w_size == 3) { - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 2, 1, 1, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 2, 1, 1, 5, 6, 10><<>>(g); + }else if (kernel_t_size ==3 && kernel_h_size == 5 && kernel_w_size == 5){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 1, 2, 2, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 1, 2, 2, 5, 6, 10><<>>(g); - }else if (kernel_t_size ==3 && kernel_h_size == 5 && kernel_w_size == 5){ - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 1, 2, 2, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 1, 2, 2, 5, 6, 10><<>>(g); + } else if (kernel_t_size ==5 && kernel_h_size == 3 && kernel_w_size == 5){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 2, 1, 2, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 2, 1, 2, 5, 6, 10><<>>(g); - } else if (kernel_t_size ==5 && kernel_h_size == 3 && kernel_w_size == 5){ - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 2, 1, 2, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 2, 1, 2, 5, 6, 10><<>>(g); + } else if (kernel_t_size == 5 && kernel_h_size == 5 && kernel_w_size == 5){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 2, 2, 2, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 2, 2, 2, 5, 6, 10><<>>(g); - } else if (kernel_t_size == 5 && kernel_h_size == 5 && kernel_w_size == 5){ + } else if (kernel_t_size == 5 && kernel_h_size == 5 && kernel_w_size == 7){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 2, 2, 3, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 2, 2, 3, 5, 6, 10><<>>(g); + } else if (kernel_t_size == 5 && kernel_h_size == 6 && kernel_w_size == 10){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 2, 3, 5, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 2, 3, 5, 5, 6, 10><<>>(g); + } else if (kernel_t_size == 5 && kernel_h_size == 1 && kernel_w_size == 1){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 2, 0, 0, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 2, 0, 0, 5, 6, 10><<>>(g); + } else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 10){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 0, 3, 5, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true, 0, 3, 5, 5, 6, 10><<>>(g); + } else if (kernel_t_size == 5 && kernel_h_size == 1 && kernel_w_size == 10){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, true, 2, 0, 5, 5, 6, 10>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, true,2, 0, 5, 5, 6, 10><<>>(g); + } else { + // print error + std::cout << "Invalid kernel size" << std::endl; + //print kernel size + std::cout << "Kernel size: " << kernel_t_size << " " << kernel_h_size << " " << kernel_w_size << std::endl; + } + } else { cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 2, 2, 2, 5, 6, 10>, + fwd_attend_ker<128, false, true, true, 1, 1, 1, 5, 6, 10>, cudaFuncAttributeMaxDynamicSharedMemorySize, mem_size ); - fwd_attend_ker<128, false, false, 2, 2, 2, 5, 6, 10><<>>(g); - - } else if (kernel_t_size == 5 && kernel_h_size == 5 && kernel_w_size == 7){ - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 2, 2, 3, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 2, 2, 3, 5, 6, 10><<>>(g); - } else if (kernel_t_size == 5 && kernel_h_size == 6 && kernel_w_size == 10){ - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 2, 3, 5, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 2, 3, 5, 5, 6, 10><<>>(g); - } else if (kernel_t_size == 5 && kernel_h_size == 1 && kernel_w_size == 1){ - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 2, 0, 0, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 2, 0, 0, 5, 6, 10><<>>(g); - } else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 10){ - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 0, 3, 5, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 0, 3, 5, 5, 6, 10><<>>(g); - } else if (kernel_t_size == 5 && kernel_h_size == 1 && kernel_w_size == 10){ - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 2, 0, 5, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 2, 0, 5, 5, 6, 10><<>>(g); - } else if (kernel_t_size == 5 && kernel_h_size == 6 && kernel_w_size == 1){ - cudaFuncSetAttribute( - fwd_attend_ker<128, false, false, 2, 3, 0, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, false, 2, 3, 0, 5, 6, 10><<>>(g); + fwd_attend_ker<128, false, true, true, 1, 1, 1, 5, 6, 10><<>>(g); } - else { + + } else { + dim3 grid_image(seq_len/(CONSUMER_WARPGROUPS*kittens::TILE_ROW_DIM*4), qo_heads, batch); + + if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 3) { + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 1, 1, 1, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 1, 1, 1, 6, 6, 6><<>>(g); + + } else if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 6) { + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 1, 1, 3, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false,1, 1, 3, 6, 6, 6><<>>(g); + + } else if (kernel_t_size == 6 && kernel_h_size == 3 && kernel_w_size == 3) { + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 3, 1, 1, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 3, 1, 1, 6, 6, 6><<>>(g); + + } else if (kernel_t_size ==3 && kernel_h_size == 6 && kernel_w_size == 6){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 1, 3, 3, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 1, 3, 3, 6, 6, 6><<>>(g); + + }else if (kernel_t_size ==3 && kernel_h_size == 6 && kernel_w_size == 3){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 1, 3, 1, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 1, 3, 1, 6, 6, 6><<>>(g); + + } else if (kernel_t_size ==6 && kernel_h_size == 3 && kernel_w_size == 6){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 3, 1, 3, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 3, 1, 3, 6, 6, 6><<>>(g); + + } else if (kernel_t_size == 6 && kernel_h_size == 6 && kernel_w_size == 6){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 3, 3, 3, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 3, 3, 3, 6, 6, 6><<>>(g); + } else if (kernel_t_size == 6 && kernel_h_size == 1 && kernel_w_size == 1){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 3, 0, 0, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 3, 0, 0, 6, 6, 6><<>>(g); + } else if (kernel_t_size == 6 && kernel_h_size == 1 && kernel_w_size == 6){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 3, 0, 3, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 3, 0, 3, 6, 6, 6><<>>(g); + } else if (kernel_t_size == 6 && kernel_h_size == 6 && kernel_w_size == 1){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 3, 3, 0, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 3, 3, 0, 6, 6, 6><<>>(g); + } else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 6){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 0, 3, 3, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 0, 3, 3, 6, 6, 6><<>>(g); + } else if (kernel_t_size == 1 && kernel_h_size == 1 && kernel_w_size == 6){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 0, 0, 3, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 0, 0, 3, 6, 6, 6><<>>(g); + } else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 1){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 0, 3, 0, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 0, 3, 0, 6, 6, 6><<>>(g); + } else if (kernel_t_size == 6 && kernel_h_size == 6 && kernel_w_size == 1){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 3, 3, 0, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 3, 3, 0, 6, 6, 6><<>>(g); + } else if (kernel_t_size == 6 && kernel_h_size == 1 && kernel_w_size == 6){ + cudaFuncSetAttribute( + fwd_attend_ker<128, false, false, false, 3, 0, 3, 6, 6, 6>, + cudaFuncAttributeMaxDynamicSharedMemorySize, + mem_size + ); + fwd_attend_ker<128, false, false, false, 3, 0, 3, 6, 6, 6><<>>(g); + } else { // print error std::cout << "Invalid kernel size" << std::endl; //print kernel size std::cout << "Kernel size: " << kernel_t_size << " " << kernel_h_size << " " << kernel_w_size << std::endl; } - } else { - cudaFuncSetAttribute( - fwd_attend_ker<128, false, true, 1, 1, 1, 5, 6, 10>, - cudaFuncAttributeMaxDynamicSharedMemorySize, - mem_size - ); - fwd_attend_ker<128, false, true, 1, 1, 1, 5, 6, 10><<>>(g); + } - - CHECK_CUDA_ERROR(cudaGetLastError()); cudaStreamSynchronize(stream); } diff --git a/csrc/sliding_tile_attention/test/bench.py b/csrc/sliding_tile_attention/test/bench.py new file mode 100644 index 000000000..5b1727299 --- /dev/null +++ b/csrc/sliding_tile_attention/test/bench.py @@ -0,0 +1,151 @@ +import os +from collections import defaultdict + +import matplotlib.pyplot as plt +import numpy as np +import torch +from st_attn import sliding_tile_attention + + +def flops(batch, seqlen, nheads, headdim, causal, mode="fwd"): + assert mode in ["fwd", "bwd", "fwd_bwd"] + f = 4 * batch * seqlen**2 * nheads * headdim // (2 if causal else 1) + return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f) + + +def efficiency(flop, time): + flop = flop / 1e12 + time = time / 1e6 + return flop / time + + +def benchmark_attention(configurations): + results = {'fwd': defaultdict(list), 'bwd': defaultdict(list)} + + for B, H, N, D, causal in configurations: + print("=" * 60) + print(f"Timing forward and backward pass for B={B}, H={H}, N={N}, D={D}, causal={causal}") + + q = torch.randn(B, H, N, D, dtype=torch.bfloat16, device='cuda', requires_grad=False).contiguous() + k = torch.randn(B, H, N, D, dtype=torch.bfloat16, device='cuda', requires_grad=False).contiguous() + v = torch.randn(B, H, N, D, dtype=torch.bfloat16, device='cuda', requires_grad=False).contiguous() + + grad_output = torch.randn_like(q, requires_grad=False).contiguous() + + qg = torch.zeros_like(q, requires_grad=False, dtype=torch.float).contiguous() + kg = torch.zeros_like(k, requires_grad=False, dtype=torch.float).contiguous() + vg = torch.zeros_like(v, requires_grad=False, dtype=torch.float).contiguous() + + # Prepare for timing forward pass + start_events_fwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)] + end_events_fwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)] + + torch.cuda.empty_cache() + torch.cuda.synchronize() + + # Warmup for forward pass + for _ in range(10): + o = sliding_tile_attention(q, k, v, [[6, 6, 6]] * 24, 0, False) + + # Time the forward pass + for i in range(10): + start_events_fwd[i].record() + o = sliding_tile_attention(q, k, v, [[6, 6, 6]] * 24, 0, False) + end_events_fwd[i].record() + + torch.cuda.synchronize() + times_fwd = [s.elapsed_time(e) for s, e in zip(start_events_fwd, end_events_fwd)] + time_us_fwd = np.mean(times_fwd) * 1000 + + tflops_fwd = efficiency(flops(B, N, H, D, causal, 'fwd'), time_us_fwd) + results['fwd'][(D, causal)].append((N, tflops_fwd)) + + print(f"Average time for forward pass in us: {time_us_fwd:.2f}") + print(f"Average efficiency for forward pass in TFLOPS: {tflops_fwd}") + print("-" * 60) + + # torch.cuda.empty_cache() + # torch.cuda.synchronize() + + # # Prepare for timing backward pass + # start_events_bwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)] + # end_events_bwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)] + + # # Warmup for backward pass + # for _ in range(10): + # qg, kg, vg = tk.mha_backward(q, k, v, o, l_vec, grad_output, causal) + + # # Time the backward pass + # for i in range(10): + # start_events_bwd[i].record() + # qg, kg, vg = tk.mha_backward(q, k, v, o, l_vec, grad_output, causal) + # end_events_bwd[i].record() + + # torch.cuda.synchronize() + # times_bwd = [s.elapsed_time(e) for s, e in zip(start_events_bwd, end_events_bwd)] + # time_us_bwd = np.mean(times_bwd) * 1000 + + # tflops_bwd = efficiency(flops(B, N, H, D, causal, 'bwd'), time_us_bwd) + # results['bwd'][(D, causal)].append((N, tflops_bwd)) + + # print(f"Average time for backward pass in us: {time_us_bwd:.2f}") + # print(f"Average efficiency for backward pass in TFLOPS: {tflops_bwd}") + print("=" * 60) + + torch.cuda.empty_cache() + torch.cuda.synchronize() + + return results + + +def plot_results(results): + os.makedirs('benchmark_results', exist_ok=True) + for mode in ['fwd', 'bwd']: + for (D, causal), values in results[mode].items(): + seq_lens = [x[0] for x in values] + tflops = [x[1] for x in values] + + plt.figure(figsize=(10, 6)) + bars = plt.bar(range(len(seq_lens)), tflops, tick_label=seq_lens) + plt.xlabel('Sequence Length') + plt.ylabel('TFLOPS') + plt.title(f'{mode.upper()} Pass - Head Dim: {D}, Causal: {causal}') + plt.grid(True) + + # Adding the numerical y value on top of each bar + for bar in bars: + yval = bar.get_height() + plt.text(bar.get_x() + bar.get_width() / 2, yval, round(yval, 2), ha='center', va='bottom') + + filename = f'benchmark_results/{mode}_D{D}_causal{causal}.png' + plt.savefig(filename) + plt.close() + + +# Example list of configurations to test +configurations = [ + (2, 24, 82944, 128, False), + # (16, 16, 768*16, 128, False), + # (16, 16, 768*2, 128, False), + # (16, 16, 768*4, 128, False), + # (16, 16, 768*8, 128, False), + # (16, 16, 768*16, 128, False), + # (16, 16, 768, 128, True), + # (16, 16, 768*2, 128, True), + # (16, 16, 768*4, 128, True), + # (16, 16, 768*8, 128, True), + # (16, 16, 768*16, 128, True), + # (16, 32, 768, 64, False), + # (16, 32, 768*2, 64, False), + # (16, 32, 768*4, 64, False), + # (16, 32, 768*8, 64, False), + # (16, 32, 768*16, 64, False), + # (16, 32, 768, 64, True), + # (16, 32, 768*2, 64, True), + # (16, 32, 768*4, 64, True), + # (16, 32, 768*8, 64, True), + # (16, 32, 768*16, 64, True), +] + +results = benchmark_attention(configurations) +# plot_results(results) diff --git a/csrc/sliding_tile_attention/test/flex_sta_ref.py b/csrc/sliding_tile_attention/test/flex_sta_ref.py index e605c0219..d7d9528a6 100644 --- a/csrc/sliding_tile_attention/test/flex_sta_ref.py +++ b/csrc/sliding_tile_attention/test/flex_sta_ref.py @@ -26,8 +26,7 @@ def generate_sta_mask(canvas_twh, kernel_twh, tile_twh, text_length): canvas_tile_t, canvas_tile_h, canvas_tile_w = canvas_t // tile_t_size, canvas_h // tile_h_size, canvas_w // tile_w_size img_seq_len = canvas_t * canvas_h * canvas_w - def get_tile_t_x_y( - idx: IntTensor) -> Tuple[IntTensor, IntTensor, IntTensor]: + def get_tile_t_x_y(idx: IntTensor) -> Tuple[IntTensor, IntTensor, IntTensor]: tile_id = idx // total_tile_size tile_t = tile_id // (canvas_tile_h * canvas_tile_w) tile_h = (tile_id % (canvas_tile_h * canvas_tile_w)) // canvas_tile_w @@ -44,37 +43,29 @@ def generate_sta_mask(canvas_twh, kernel_twh, tile_twh, text_length): kv_t_tile, kv_x_tile, kv_y_tile = get_tile_t_x_y(kv_idx) # kernel nominally attempts to center itself on the query, but kernel center # is clamped to a fixed distance (kernel half-length) from the canvas edge - kernel_center_t = q_t_tile.clamp(kernel_t // 2, - (canvas_tile_t - 1) - kernel_t // 2) - kernel_center_x = q_x_tile.clamp(kernel_h // 2, - (canvas_tile_h - 1) - kernel_h // 2) - kernel_center_y = q_y_tile.clamp(kernel_w // 2, - (canvas_tile_w - 1) - kernel_w // 2) + kernel_center_t = q_t_tile.clamp(kernel_t // 2, (canvas_tile_t - 1) - kernel_t // 2) + kernel_center_x = q_x_tile.clamp(kernel_h // 2, (canvas_tile_h - 1) - kernel_h // 2) + kernel_center_y = q_y_tile.clamp(kernel_w // 2, (canvas_tile_w - 1) - kernel_w // 2) time_mask = (kernel_center_t - kv_t_tile).abs() <= kernel_t // 2 hori_mask = (kernel_center_x - kv_x_tile).abs() <= kernel_h // 2 vert_mask = (kernel_center_y - kv_y_tile).abs() <= kernel_w // 2 image_mask = (q_idx < img_seq_len) & (kv_idx < img_seq_len) - image_to_text_mask = (q_idx < img_seq_len) & ( - kv_idx >= img_seq_len) & (kv_idx < img_seq_len + text_length) - text_to_all_mask = (q_idx >= img_seq_len) & (kv_idx < - img_seq_len + text_length) - return (image_mask & time_mask & hori_mask - & vert_mask) | image_to_text_mask | text_to_all_mask + image_to_text_mask = (q_idx < img_seq_len) & (kv_idx >= img_seq_len) & (kv_idx < img_seq_len + text_length) + text_to_all_mask = (q_idx >= img_seq_len) & (kv_idx < img_seq_len + text_length) + return (image_mask & time_mask & hori_mask & vert_mask) | image_to_text_mask | text_to_all_mask sta_mask_3d.__name__ = f"natten_3d_c{canvas_t}x{canvas_w}x{canvas_h}_k{kernel_t}x{kernel_w}x{kernel_h}" return sta_mask_3d -def get_sliding_tile_attention_mask(kernel_size, tile_size, img_size, - text_length, device): +def get_sliding_tile_attention_mask(kernel_size, tile_size, img_size, text_length, device, text_max_len=256): img_seq_len = img_size[0] * img_size[1] * img_size[2] - image_mask = generate_sta_mask(img_size, kernel_size, tile_size, - text_length) + image_mask = generate_sta_mask(img_size, kernel_size, tile_size, text_length) mask = create_block_mask(image_mask, B=None, H=None, - Q_LEN=img_seq_len + 256, - KV_LEN=img_seq_len + 256, + Q_LEN=img_seq_len + text_max_len, + KV_LEN=img_seq_len + text_max_len, device=device, _compile=True) return mask diff --git a/csrc/sliding_tile_attention/test/test_sta.py b/csrc/sliding_tile_attention/test/test_sta.py index 659dc195b..ea877d107 100644 --- a/csrc/sliding_tile_attention/test/test_sta.py +++ b/csrc/sliding_tile_attention/test/test_sta.py @@ -9,15 +9,14 @@ flex_attention = torch.compile(flex_attention, dynamic=False) def flex_test(Q, K, V, kernel_size): - mask = get_sliding_tile_attention_mask(kernel_size, (6, 8, 8), - (30, 48, 80), 39, 'cuda') + mask = get_sliding_tile_attention_mask(kernel_size, (6, 8, 8), (36, 48, 48), 39, 'cuda', 0) output = flex_attention(Q, K, V, block_mask=mask) return output def h100_fwd_kernel_test(Q, K, V, kernel_size): - o = sliding_tile_attention(Q, K, V, [kernel_size] * 24, 39) + o = sliding_tile_attention(Q, K, V, [kernel_size] * 24, 39, False) return o @@ -25,22 +24,12 @@ def generate_tensor(shape, mean, std, dtype, device): tensor = torch.randn(shape, dtype=dtype, device=device) magnitude = torch.norm(tensor, dim=-1, keepdim=True) - scaled_tensor = tensor * ( - torch.randn(magnitude.shape, dtype=dtype, device=device) * std + - mean) / magnitude + scaled_tensor = tensor * (torch.randn(magnitude.shape, dtype=dtype, device=device) * std + mean) / magnitude return scaled_tensor.contiguous() -def check_correctness(b, - h, - n, - d, - causal, - mean, - std, - num_iterations=50, - error_mode='all'): +def check_correctness(b, h, n, d, causal, mean, std, num_iterations=50, error_mode='all'): results = { 'TK vs FLEX': { 'sum_diff': 0, @@ -48,40 +37,33 @@ def check_correctness(b, 'max_diff': 0 }, } - kernel_size_ls = [(1, 6, 10), (5, 1, 1), (3, 3, 3), (5, 5, 5)] + kernel_size_ls = [(6, 1, 6), (6, 6, 1)] from tqdm import tqdm for kernel_size in tqdm(kernel_size_ls): for _ in range(num_iterations): torch.manual_seed(0) - Q = generate_tensor((b, h, n, d), mean, std, torch.bfloat16, - 'cuda') - K = generate_tensor((b, h, n, d), mean, std, torch.bfloat16, - 'cuda') - V = generate_tensor((b, h, n, d), mean, std, torch.bfloat16, - 'cuda') + Q = generate_tensor((b, h, n, d), mean, std, torch.bfloat16, 'cuda') + K = generate_tensor((b, h, n, d), mean, std, torch.bfloat16, 'cuda') + V = generate_tensor((b, h, n, d), mean, std, torch.bfloat16, 'cuda') tk_o = h100_fwd_kernel_test(Q, K, V, kernel_size) pt_o = flex_test(Q, K, V, kernel_size) diff = pt_o - tk_o abs_diff = torch.abs(diff) results['TK vs FLEX']['sum_diff'] += torch.sum(abs_diff).item() - results['TK vs FLEX']['max_diff'] = max( - results['TK vs FLEX']['max_diff'], - torch.max(abs_diff).item()) + results['TK vs FLEX']['max_diff'] = max(results['TK vs FLEX']['max_diff'], torch.max(abs_diff).item()) torch.cuda.empty_cache() print("kernel_size", kernel_size) print("max_diff", torch.max(abs_diff).item()) print( "avg_diff", - torch.sum(abs_diff).item() / - (b * h * n * d * (1 if error_mode == 'output' else - 3 if error_mode == 'backward' else 4))) + torch.sum(abs_diff).item() / (b * h * n * d * + (1 if error_mode == 'output' else 3 if error_mode == 'backward' else 4))) - total_elements = b * h * n * d * num_iterations * ( - 1 if error_mode == 'output' else - 3 if error_mode == 'backward' else 4) * len(kernel_size_ls) + total_elements = b * h * n * d * num_iterations * (1 if error_mode == 'output' else + 3 if error_mode == 'backward' else 4) * len(kernel_size_ls) for name, data in results.items(): avg_diff = data['sum_diff'] / total_elements max_diff = data['max_diff'] @@ -91,26 +73,19 @@ def check_correctness(b, def generate_error_graphs(b, h, d, causal, mean, std, error_mode='all'): - seq_lengths = [115456] + seq_lengths = [82944] tk_avg_errors, tk_max_errors = [], [] for n in tqdm(seq_lengths, desc="Generating error data"): - results = check_correctness(b, - h, - n, - d, - causal, - mean, - std, - error_mode=error_mode) + results = check_correctness(b, h, n, d, causal, mean, std, error_mode=error_mode) tk_avg_errors.append(results['TK vs FLEX']['avg_diff']) tk_max_errors.append(results['TK vs FLEX']['max_diff']) # Example usage -b, h, d = 1, 24, 128 +b, h, d = 2, 24, 128 causal = False mean = 1e-1 std = 10 diff --git a/demo/gradio_web_demo.py b/demo/gradio_web_demo.py index 8396e3d07..f972bf5fe 100644 --- a/demo/gradio_web_demo.py +++ b/demo/gradio_web_demo.py @@ -23,9 +23,7 @@ def init_args(): parser.add_argument("--model_path", type=str, default="data/mochi") parser.add_argument("--seed", type=int, default=12345) parser.add_argument("--transformer_path", type=str, default=None) - parser.add_argument("--scheduler_type", - type=str, - default="pcm_linear_quadratic") + parser.add_argument("--scheduler_type", type=str, default="pcm_linear_quadratic") parser.add_argument("--lora_checkpoint_dir", type=str, default=None) parser.add_argument("--shift", type=float, default=8.0) parser.add_argument("--num_euler_timesteps", type=int, default=50) @@ -50,15 +48,11 @@ def load_model(args): ) if args.transformer_path: - transformer = MochiTransformer3DModel.from_pretrained( - args.transformer_path) + transformer = MochiTransformer3DModel.from_pretrained(args.transformer_path) else: - transformer = MochiTransformer3DModel.from_pretrained( - args.model_path, subfolder="transformer/") + transformer = MochiTransformer3DModel.from_pretrained(args.model_path, subfolder="transformer/") - pipe = MochiPipeline.from_pretrained(args.model_path, - transformer=transformer, - scheduler=scheduler) + pipe = MochiPipeline.from_pretrained(args.model_path, transformer=transformer, scheduler=scheduler) pipe.enable_vae_tiling() # pipe.to(device) # if args.cpu_offload: @@ -137,11 +131,7 @@ with gr.Blocks() as demo: step=32, value=args.height, ) - width = gr.Slider(label="Width", - minimum=256, - maximum=1024, - step=32, - value=args.width) + width = gr.Slider(label="Width", minimum=256, maximum=1024, step=32, value=args.width) with gr.Row(): num_frames = gr.Slider( @@ -164,8 +154,7 @@ with gr.Blocks() as demo: ) with gr.Row(): - use_negative_prompt = gr.Checkbox(label="Use negative prompt", - value=False) + use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) negative_prompt = gr.Text( label="Negative prompt", max_lines=1, @@ -173,11 +162,7 @@ with gr.Blocks() as demo: visible=False, ) - seed = gr.Slider(label="Seed", - minimum=0, - maximum=1000000, - step=1, - value=args.seed) + seed = gr.Slider(label="Seed", minimum=0, maximum=1000000, step=1, value=args.seed) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) seed_output = gr.Number(label="Used Seed") diff --git a/fastvideo/data_preprocess/preprocess_text_embeddings.py b/fastvideo/data_preprocess/preprocess_text_embeddings.py index 909bd38a3..34484a231 100644 --- a/fastvideo/data_preprocess/preprocess_text_embeddings.py +++ b/fastvideo/data_preprocess/preprocess_text_embeddings.py @@ -27,8 +27,7 @@ class T5dataset(Dataset): self.vae_debug = vae_debug with open(self.json_path, "r") as f: train_dataset = json.load(f) - self.train_dataset = sorted(train_dataset, - key=lambda x: x["latent_path"]) + self.train_dataset = sorted(train_dataset, key=lambda x: x["latent_path"]) def __getitem__(self, idx): caption = self.train_dataset[idx]["caption"] @@ -36,17 +35,13 @@ class T5dataset(Dataset): length = self.train_dataset[idx]["length"] if self.vae_debug: latents = torch.load( - os.path.join(args.output_dir, "latent", - self.train_dataset[idx]["latent_path"]), + os.path.join(args.output_dir, "latent", self.train_dataset[idx]["latent_path"]), map_location="cpu", ) else: latents = [] - return dict(caption=caption, - latents=latents, - filename=filename, - length=length) + return dict(caption=caption, latents=latents, filename=filename, length=length) def __len__(self): return len(self.train_dataset) @@ -60,31 +55,21 @@ def main(args): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") torch.cuda.set_device(local_rank) if not dist.is_initialized(): - dist.init_process_group(backend="nccl", - init_method="env://", - world_size=world_size, - rank=local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) videoprocessor = VideoProcessor(vae_scale_factor=8) os.makedirs(args.output_dir, exist_ok=True) os.makedirs(os.path.join(args.output_dir, "video"), exist_ok=True) os.makedirs(os.path.join(args.output_dir, "latent"), exist_ok=True) os.makedirs(os.path.join(args.output_dir, "prompt_embed"), exist_ok=True) - os.makedirs(os.path.join(args.output_dir, "prompt_attention_mask"), - exist_ok=True) + os.makedirs(os.path.join(args.output_dir, "prompt_attention_mask"), exist_ok=True) - latents_json_path = os.path.join(args.output_dir, - "videos2caption_temp.json") + latents_json_path = os.path.join(args.output_dir, "videos2caption_temp.json") train_dataset = T5dataset(latents_json_path, args.vae_debug) - text_encoder = load_text_encoder(args.model_type, - args.model_path, - device=device) + text_encoder = load_text_encoder(args.model_type, args.model_path, device=device) vae, autocast_type, fps = load_vae(args.model_type, args.model_path) vae.enable_tiling() - sampler = DistributedSampler(train_dataset, - rank=local_rank, - num_replicas=world_size, - shuffle=True) + sampler = DistributedSampler(train_dataset, rank=local_rank, num_replicas=world_size, shuffle=True) train_dataloader = DataLoader( train_dataset, sampler=sampler, @@ -96,26 +81,19 @@ def main(args): for _, data in tqdm(enumerate(train_dataloader), disable=local_rank != 0): with torch.inference_mode(): with torch.autocast("cuda", dtype=autocast_type): - prompt_embeds, prompt_attention_mask = text_encoder.encode_prompt( - prompt=data["caption"], ) + prompt_embeds, prompt_attention_mask = text_encoder.encode_prompt(prompt=data["caption"], ) if args.vae_debug: latents = data["latents"] - video = vae.decode(latents.to(device), - return_dict=False)[0] + video = vae.decode(latents.to(device), return_dict=False)[0] video = videoprocessor.postprocess_video(video) for idx, video_name in enumerate(data["filename"]): - prompt_embed_path = os.path.join(args.output_dir, - "prompt_embed", - video_name + ".pt") - video_path = os.path.join(args.output_dir, "video", - video_name + ".mp4") - prompt_attention_mask_path = os.path.join( - args.output_dir, "prompt_attention_mask", - video_name + ".pt") + prompt_embed_path = os.path.join(args.output_dir, "prompt_embed", video_name + ".pt") + video_path = os.path.join(args.output_dir, "video", video_name + ".mp4") + prompt_attention_mask_path = os.path.join(args.output_dir, "prompt_attention_mask", + video_name + ".pt") # save latent torch.save(prompt_embeds[idx], prompt_embed_path) - torch.save(prompt_attention_mask[idx], - prompt_attention_mask_path) + torch.save(prompt_attention_mask[idx], prompt_attention_mask_path) print(f"sample {video_name} saved") if args.vae_debug: export_to_video(video[idx], video_path, fps=fps) @@ -133,8 +111,7 @@ def main(args): if local_rank == 0: # os.remove(latents_json_path) all_json_data = [item for sublist in gathered_data for item in sublist] - with open(os.path.join(args.output_dir, "videos2caption.json"), - "w") as f: + with open(os.path.join(args.output_dir, "videos2caption.json"), "w") as f: json.dump(all_json_data, f, indent=4) @@ -148,8 +125,7 @@ if __name__ == "__main__": "--dataloader_num_workers", type=int, default=1, - help= - "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--train_batch_size", @@ -157,16 +133,13 @@ if __name__ == "__main__": default=1, help="Batch size (per device) for the training dataloader.", ) - parser.add_argument("--text_encoder_name", - type=str, - default="google/t5-v1_1-xxl") + parser.add_argument("--text_encoder_name", type=str, default="google/t5-v1_1-xxl") parser.add_argument("--cache_dir", type=str, default="./cache_dir") parser.add_argument( "--output_dir", type=str, default=None, - help= - "The output directory where the model predictions and checkpoints will be written.", + help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument("--vae_debug", action="store_true") args = parser.parse_args() diff --git a/fastvideo/data_preprocess/preprocess_vae_latents.py b/fastvideo/data_preprocess/preprocess_vae_latents.py index 166206a20..9ed31f624 100644 --- a/fastvideo/data_preprocess/preprocess_vae_latents.py +++ b/fastvideo/data_preprocess/preprocess_vae_latents.py @@ -20,10 +20,7 @@ def main(args): world_size = int(os.getenv("WORLD_SIZE", 1)) print("world_size", world_size, "local rank", local_rank) train_dataset = getdataset(args) - sampler = DistributedSampler(train_dataset, - rank=local_rank, - num_replicas=world_size, - shuffle=True) + sampler = DistributedSampler(train_dataset, rank=local_rank, num_replicas=world_size, shuffle=True) train_dataloader = DataLoader( train_dataset, sampler=sampler, @@ -31,14 +28,10 @@ def main(args): num_workers=args.dataloader_num_workers, ) - encoder_device = torch.device( - "cuda" if torch.cuda.is_available() else "cpu") + encoder_device = torch.device("cuda" if torch.cuda.is_available() else "cpu") torch.cuda.set_device(local_rank) if not dist.is_initialized(): - dist.init_process_group(backend="nccl", - init_method="env://", - world_size=world_size, - rank=local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) vae, autocast_type, fps = load_vae(args.model_type, args.model_path) vae.enable_tiling() os.makedirs(args.output_dir, exist_ok=True) @@ -48,12 +41,10 @@ def main(args): for _, data in tqdm(enumerate(train_dataloader), disable=local_rank != 0): with torch.inference_mode(): with torch.autocast("cuda", dtype=autocast_type): - latents = vae.encode(data["pixel_values"].to( - encoder_device))["latent_dist"].sample() + latents = vae.encode(data["pixel_values"].to(encoder_device))["latent_dist"].sample() for idx, video_path in enumerate(data["path"]): video_name = os.path.basename(video_path).split(".")[0] - latent_path = os.path.join(args.output_dir, "latent", - video_name + ".pt") + latent_path = os.path.join(args.output_dir, "latent", video_name + ".pt") torch.save(latents[idx].to(torch.bfloat16), latent_path) item = {} item["length"] = latents[idx].shape[1] @@ -67,8 +58,7 @@ def main(args): dist.all_gather_object(gathered_data, local_data) if local_rank == 0: all_json_data = [item for sublist in gathered_data for item in sublist] - with open(os.path.join(args.output_dir, "videos2caption_temp.json"), - "w") as f: + with open(os.path.join(args.output_dir, "videos2caption_temp.json"), "w") as f: json.dump(all_json_data, f, indent=4) @@ -83,8 +73,7 @@ if __name__ == "__main__": "--dataloader_num_workers", type=int, default=1, - help= - "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--train_batch_size", @@ -92,15 +81,10 @@ if __name__ == "__main__": default=16, help="Batch size (per device) for the training dataloader.", ) - parser.add_argument("--num_latent_t", - type=int, - default=28, - help="Number of latent timesteps.") + parser.add_argument("--num_latent_t", type=int, default=28, help="Number of latent timesteps.") parser.add_argument("--max_height", type=int, default=480) parser.add_argument("--max_width", type=int, default=848) - parser.add_argument("--video_length_tolerance_range", - type=int, - default=2.0) + parser.add_argument("--video_length_tolerance_range", type=int, default=2.0) parser.add_argument("--group_frame", action="store_true") # TODO parser.add_argument("--group_resolution", action="store_true") # TODO parser.add_argument("--dataset", default="t2v") @@ -110,25 +94,21 @@ if __name__ == "__main__": parser.add_argument("--speed_factor", type=float, default=1.0) parser.add_argument("--drop_short_ratio", type=float, default=1.0) # text encoder & vae & diffusion model - parser.add_argument("--text_encoder_name", - type=str, - default="google/t5-v1_1-xxl") + parser.add_argument("--text_encoder_name", type=str, default="google/t5-v1_1-xxl") parser.add_argument("--cache_dir", type=str, default="./cache_dir") parser.add_argument("--cfg", type=float, default=0.0) parser.add_argument( "--output_dir", type=str, default=None, - help= - "The output directory where the model predictions and checkpoints will be written.", + help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--logging_dir", type=str, default="logs", - help= - ("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), + help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), ) args = parser.parse_args() diff --git a/fastvideo/data_preprocess/preprocess_validation_text_embeddings.py b/fastvideo/data_preprocess/preprocess_validation_text_embeddings.py index ecfb71787..8733307e5 100644 --- a/fastvideo/data_preprocess/preprocess_validation_text_embeddings.py +++ b/fastvideo/data_preprocess/preprocess_validation_text_embeddings.py @@ -18,14 +18,9 @@ def main(args): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") torch.cuda.set_device(local_rank) if not dist.is_initialized(): - dist.init_process_group(backend="nccl", - init_method="env://", - world_size=world_size, - rank=local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) - text_encoder = load_text_encoder(args.model_type, - args.model_path, - device=device) + text_encoder = load_text_encoder(args.model_type, args.model_path, device=device) autocast_type = torch.float16 if args.model_type == "hunyuan" else torch.bfloat16 # output_dir/validation/prompt_attention_mask # output_dir/validation/prompt_embed @@ -34,8 +29,7 @@ def main(args): os.path.join(args.output_dir, "validation", "prompt_attention_mask"), exist_ok=True, ) - os.makedirs(os.path.join(args.output_dir, "validation", "prompt_embed"), - exist_ok=True) + os.makedirs(os.path.join(args.output_dir, "validation", "prompt_embed"), exist_ok=True) with open(args.validation_prompt_txt, "r", encoding="utf-8") as file: lines = file.readlines() @@ -43,12 +37,9 @@ def main(args): for prompt in prompts: with torch.inference_mode(): with torch.autocast("cuda", dtype=autocast_type): - prompt_embeds, prompt_attention_mask = text_encoder.encode_prompt( - prompt) + prompt_embeds, prompt_attention_mask = text_encoder.encode_prompt(prompt) file_name = prompt.split(".")[0] - prompt_embed_path = os.path.join(args.output_dir, "validation", - "prompt_embed", - f"{file_name}.pt") + prompt_embed_path = os.path.join(args.output_dir, "validation", "prompt_embed", f"{file_name}.pt") prompt_attention_mask_path = os.path.join( args.output_dir, "validation", @@ -56,8 +47,7 @@ def main(args): f"{file_name}.pt", ) torch.save(prompt_embeds[0], prompt_embed_path) - torch.save(prompt_attention_mask[0], - prompt_attention_mask_path) + torch.save(prompt_attention_mask[0], prompt_attention_mask_path) print(f"sample {file_name} saved") @@ -71,8 +61,7 @@ if __name__ == "__main__": "--output_dir", type=str, default=None, - help= - "The output directory where the model predictions and checkpoints will be written.", + help="The output directory where the model predictions and checkpoints will be written.", ) args = parser.parse_args() main(args) diff --git a/fastvideo/dataset/__init__.py b/fastvideo/dataset/__init__.py index 781f232ad..37ef62f42 100644 --- a/fastvideo/dataset/__init__.py +++ b/fastvideo/dataset/__init__.py @@ -3,16 +3,14 @@ from torchvision.transforms import Lambda from transformers import AutoTokenizer from fastvideo.dataset.t2v_datasets import T2V_dataset -from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255, - TemporalRandomCrop) +from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255, TemporalRandomCrop) def getdataset(args): temporal_sample = TemporalRandomCrop(args.num_frames) # 16 x norm_fun = Lambda(lambda x: 2.0 * x - 1.0) resize_topcrop = [ - CenterCropResizeVideo((args.max_height, args.max_width), - top_crop=True), + CenterCropResizeVideo((args.max_height, args.max_width), top_crop=True), ] resize = [ CenterCropResizeVideo((args.max_height, args.max_width)), @@ -27,8 +25,7 @@ def getdataset(args): norm_fun, ]) # tokenizer = AutoTokenizer.from_pretrained("/storage/ongoing/new/Open-Sora-Plan/cache_dir/mt5-xxl", cache_dir=args.cache_dir) - tokenizer = AutoTokenizer.from_pretrained(args.text_encoder_name, - cache_dir=args.cache_dir) + tokenizer = AutoTokenizer.from_pretrained(args.text_encoder_name, cache_dir=args.cache_dir) if args.dataset == "t2v": return T2V_dataset( args, @@ -66,8 +63,7 @@ if __name__ == "__main__": "interpolation_scale_h": 1, "interpolation_scale_w": 1, "cache_dir": "../cache_dir", - "image_data": - "/storage/ongoing/new/Open-Sora-Plan-bak/7.14bak/scripts/train_data/image_data.txt", + "image_data": "/storage/ongoing/new/Open-Sora-Plan-bak/7.14bak/scripts/train_data/image_data.txt", "video_data": "1", "train_fps": 24, "drop_short_ratio": 1.0, @@ -84,10 +80,7 @@ if __name__ == "__main__": zero = 0 for idx in tqdm(range(num)): image_data = dataset_prog.img_cap_list[idx] - caps = [ - i["cap"] if isinstance(i["cap"], list) else [i["cap"]] - for i in image_data - ] + caps = [i["cap"] if isinstance(i["cap"], list) else [i["cap"]] for i in image_data] try: caps = [[random.choice(i)] for i in caps] except Exception as e: diff --git a/fastvideo/dataset/latent_datasets.py b/fastvideo/dataset/latent_datasets.py index 667ea515e..1ef389d91 100644 --- a/fastvideo/dataset/latent_datasets.py +++ b/fastvideo/dataset/latent_datasets.py @@ -20,10 +20,8 @@ class LatentDataset(Dataset): self.datase_dir_path = os.path.dirname(json_path) self.video_dir = os.path.join(self.datase_dir_path, "video") self.latent_dir = os.path.join(self.datase_dir_path, "latent") - self.prompt_embed_dir = os.path.join(self.datase_dir_path, - "prompt_embed") - self.prompt_attention_mask_dir = os.path.join(self.datase_dir_path, - "prompt_attention_mask") + self.prompt_embed_dir = os.path.join(self.datase_dir_path, "prompt_embed") + self.prompt_attention_mask_dir = os.path.join(self.datase_dir_path, "prompt_attention_mask") with open(self.json_path, "r") as f: self.data_anno = json.load(f) # json.load(f) already keeps the order @@ -33,16 +31,12 @@ class LatentDataset(Dataset): self.uncond_prompt_embed = torch.zeros(256, 4096).to(torch.float32) # 256 zeros self.uncond_prompt_mask = torch.zeros(256).bool() - self.lengths = [ - data_item["length"] if "length" in data_item else 1 - for data_item in self.data_anno - ] + self.lengths = [data_item["length"] if "length" in data_item else 1 for data_item in self.data_anno] def __getitem__(self, idx): latent_file = self.data_anno[idx]["latent_path"] prompt_embed_file = self.data_anno[idx]["prompt_embed_path"] - prompt_attention_mask_file = self.data_anno[idx][ - "prompt_attention_mask"] + prompt_attention_mask_file = self.data_anno[idx]["prompt_attention_mask"] # load latent = torch.load( os.path.join(self.latent_dir, latent_file), @@ -60,8 +54,7 @@ class LatentDataset(Dataset): weights_only=True, ) prompt_attention_mask = torch.load( - os.path.join(self.prompt_attention_mask_dir, - prompt_attention_mask_file), + os.path.join(self.prompt_attention_mask_dir, prompt_attention_mask_file), map_location="cpu", weights_only=True, ) @@ -111,13 +104,8 @@ def latent_collate_function(batch): if __name__ == "__main__": - dataset = LatentDataset("data/Mochi-Synthetic-Data/merge.txt", - num_latent_t=28) - dataloader = torch.utils.data.DataLoader( - dataset, - batch_size=2, - shuffle=False, - collate_fn=latent_collate_function) + dataset = LatentDataset("data/Mochi-Synthetic-Data/merge.txt", num_latent_t=28) + dataloader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=False, collate_fn=latent_collate_function) for latent, prompt_embed, latent_attn_mask, prompt_attention_mask in dataloader: print( latent.shape, diff --git a/fastvideo/dataset/t2v_datasets.py b/fastvideo/dataset/t2v_datasets.py index d973519b0..48f96b628 100644 --- a/fastvideo/dataset/t2v_datasets.py +++ b/fastvideo/dataset/t2v_datasets.py @@ -46,8 +46,7 @@ class DataSetProg(metaclass=SingletonMeta): for i in range(self.num_workers): self.n_used_elements[i] = 0 - per_worker = int( - math.ceil(len(self.elements) / float(self.num_workers))) + per_worker = int(math.ceil(len(self.elements) / float(self.num_workers))) start = i * per_worker end = min(start + per_worker, len(self.elements)) self.worker_elements[i] = self.elements[start:end] @@ -58,9 +57,7 @@ class DataSetProg(metaclass=SingletonMeta): else: worker_id = work_info.id - idx = self.worker_elements[worker_id][ - self.n_used_elements[worker_id] % - len(self.worker_elements[worker_id])] + idx = self.worker_elements[worker_id][self.n_used_elements[worker_id] % len(self.worker_elements[worker_id])] self.n_used_elements[worker_id] += 1 return idx @@ -68,10 +65,7 @@ class DataSetProg(metaclass=SingletonMeta): dataset_prog = DataSetProg() -def filter_resolution(h, - w, - max_h_div_w_ratio=17 / 16, - min_h_div_w_ratio=8 / 16): +def filter_resolution(h, w, max_h_div_w_ratio=17 / 16, min_h_div_w_ratio=8 / 16): if h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio: return True return False @@ -79,8 +73,7 @@ def filter_resolution(h, class T2V_dataset(Dataset): - def __init__(self, args, transform, temporal_sample, tokenizer, - transform_topcrop): + def __init__(self, args, transform, temporal_sample, tokenizer, transform_topcrop): self.data = args.data_merge_path self.num_frames = args.num_frames self.train_fps = args.train_fps @@ -109,8 +102,7 @@ class T2V_dataset(Dataset): self.lengths = self.sample_num_frames n_elements = len(cap_list) - dataset_prog.set_cap_list(args.dataloader_num_workers, cap_list, - n_elements) + dataset_prog.set_cap_list(args.dataloader_num_workers, cap_list, n_elements) print(f"video length: {len(dataset_prog.cap_list)}", flush=True) @@ -137,8 +129,7 @@ class T2V_dataset(Dataset): video_path = dataset_prog.cap_list[idx]["path"] assert os.path.exists(video_path), f"file {video_path} do not exist!" frame_indices = dataset_prog.cap_list[idx]["sample_frame_index"] - torchvision_video, _, metadata = torchvision.io.read_video( - video_path, output_format="TCHW") + torchvision_video, _, metadata = torchvision.io.read_video(video_path, output_format="TCHW") video = torchvision_video[frame_indices] video = self.transform(video) video = rearrange(video, "t c h w -> c t h w") @@ -178,8 +169,7 @@ class T2V_dataset(Dataset): ) def get_image(self, idx): - image_data = dataset_prog.cap_list[ - idx] # [{'path': path, 'cap': cap}, ...] + image_data = dataset_prog.cap_list[idx] # [{'path': path, 'cap': cap}, ...] image = Image.open(image_data["path"]).convert("RGB") # [h, w, c] image = torch.from_numpy(np.array(image)) # [h, w, c] @@ -188,15 +178,13 @@ class T2V_dataset(Dataset): # h, w = i.shape[-2:] # assert h / w <= 17 / 16 and h / w >= 8 / 16, f'Only image with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But found ratio is {round(h / w, 2)} with the shape of {i.shape}' - image = (self.transform_topcrop(image) if "human_images" - in image_data["path"] else self.transform(image) + image = (self.transform_topcrop(image) if "human_images" in image_data["path"] else self.transform(image) ) # [1 C H W] -> num_img [1 C H W] image = image.transpose(0, 1) # [1 C H W] -> [C 1 H W] image = image.float() / 127.5 - 1.0 - caps = (image_data["cap"] if isinstance(image_data["cap"], list) else - [image_data["cap"]]) + caps = (image_data["cap"] if isinstance(image_data["cap"], list) else [image_data["cap"]]) caps = [random.choice(caps)] text = caps input_ids, cond_mask = [], [] @@ -250,12 +238,10 @@ class T2V_dataset(Dataset): cnt_no_resolution += 1 continue else: - if (resolution.get("height", None) is None - or resolution.get("width", None) is None): + if (resolution.get("height", None) is None or resolution.get("width", None) is None): cnt_no_resolution += 1 continue - height, width = i["resolution"]["height"], i["resolution"][ - "width"] + height, width = i["resolution"]["height"], i["resolution"]["width"] aspect = self.max_height / self.max_width hw_aspect_thr = 1.5 is_pick = filter_resolution( @@ -273,34 +259,29 @@ class T2V_dataset(Dataset): i["num_frames"] = math.ceil(fps * duration) # max 5.0 and min 1.0 are just thresholds to filter some videos which have suitable duration. if i["num_frames"] / fps > self.video_length_tolerance_range * ( - self.num_frames / self.train_fps * self.speed_factor - ): # too long video is not suitable for this training stage (self.num_frames) + self.num_frames / self.train_fps * + self.speed_factor): # too long video is not suitable for this training stage (self.num_frames) cnt_too_long += 1 continue # resample in case high fps, such as 50/60/90/144 -> train_fps(e.g, 24) frame_interval = fps / self.train_fps start_frame_idx = 0 - frame_indices = np.arange(start_frame_idx, i["num_frames"], - frame_interval).astype(int) + frame_indices = np.arange(start_frame_idx, i["num_frames"], frame_interval).astype(int) # comment out it to enable dynamic frames training - if (len(frame_indices) < self.num_frames - and random.random() < self.drop_short_ratio): + if (len(frame_indices) < self.num_frames and random.random() < self.drop_short_ratio): cnt_too_short += 1 continue # too long video will be temporal-crop randomly if len(frame_indices) > self.num_frames: - begin_index, end_index = self.temporal_sample( - len(frame_indices)) + begin_index, end_index = self.temporal_sample(len(frame_indices)) frame_indices = frame_indices[begin_index:end_index] # frame_indices = frame_indices[:self.num_frames] # head crop i["sample_frame_index"] = frame_indices.tolist() new_cap_list.append(i) - i["sample_num_frames"] = len( - i["sample_frame_index"] - ) # will use in dataloader(group sampler) + i["sample_num_frames"] = len(i["sample_frame_index"]) # will use in dataloader(group sampler) sample_num_frames.append(i["sample_num_frames"]) elif path.endswith(".jpg"): # image cnt_img += 1 @@ -309,32 +290,26 @@ class T2V_dataset(Dataset): sample_num_frames.append(i["sample_num_frames"]) else: raise NameError( - f"Unknown file extension {path.split('.')[-1]}, only support .mp4 for video and .jpg for image" - ) + f"Unknown file extension {path.split('.')[-1]}, only support .mp4 for video and .jpg for image") # import ipdb;ipdb.set_trace() main_print( f"no_cap: {cnt_no_cap}, too_long: {cnt_too_long}, too_short: {cnt_too_short}, " f"no_resolution: {cnt_no_resolution}, resolution_mismatch: {cnt_resolution_mismatch}, " f"Counter(sample_num_frames): {Counter(sample_num_frames)}, cnt_movie: {cnt_movie}, cnt_img: {cnt_img}, " - f"before filter: {len(cap_list)}, after filter: {len(new_cap_list)}" - ) + f"before filter: {len(cap_list)}, after filter: {len(new_cap_list)}") return new_cap_list, sample_num_frames def decord_read(self, path, frame_indices): decord_vr = self.v_decoder(path) video_data = decord_vr.get_batch(frame_indices).asnumpy() video_data = torch.from_numpy(video_data) - video_data = video_data.permute(0, 3, 1, - 2) # (T, H, W, C) -> (T C H W) + video_data = video_data.permute(0, 3, 1, 2) # (T, H, W, C) -> (T C H W) return video_data def read_jsons(self, data): cap_lists = [] with open(data, "r") as f: - folder_anno = [ - i.strip().split(",") for i in f.readlines() - if len(i.strip()) > 0 - ] + folder_anno = [i.strip().split(",") for i in f.readlines() if len(i.strip()) > 0] print(folder_anno) for folder, anno in folder_anno: with open(anno, "r") as f: diff --git a/fastvideo/dataset/transform.py b/fastvideo/dataset/transform.py index 319649cd7..f25842840 100644 --- a/fastvideo/dataset/transform.py +++ b/fastvideo/dataset/transform.py @@ -21,19 +21,15 @@ def center_crop_arr(pil_image, image_size): https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126 """ while min(*pil_image.size) >= 2 * image_size: - pil_image = pil_image.resize(tuple(x // 2 for x in pil_image.size), - resample=Image.BOX) + pil_image = pil_image.resize(tuple(x // 2 for x in pil_image.size), resample=Image.BOX) scale = image_size / min(*pil_image.size) - pil_image = pil_image.resize(tuple( - round(x * scale) for x in pil_image.size), - resample=Image.BICUBIC) + pil_image = pil_image.resize(tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC) arr = np.array(pil_image) crop_y = (arr.shape[0] - image_size) // 2 crop_x = (arr.shape[1] - image_size) // 2 - return Image.fromarray(arr[crop_y:crop_y + image_size, - crop_x:crop_x + image_size]) + return Image.fromarray(arr[crop_y:crop_y + image_size, crop_x:crop_x + image_size]) def crop(clip, i, j, h, w): @@ -48,9 +44,7 @@ def crop(clip, i, j, h, w): def resize(clip, target_size, interpolation_mode): if len(target_size) != 2: - raise ValueError( - f"target size should be tuple (height, width), instead got {target_size}" - ) + raise ValueError(f"target size should be tuple (height, width), instead got {target_size}") return torch.nn.functional.interpolate( clip, size=target_size, @@ -62,9 +56,7 @@ def resize(clip, target_size, interpolation_mode): def resize_scale(clip, target_size, interpolation_mode): if len(target_size) != 2: - raise ValueError( - f"target size should be tuple (height, width), instead got {target_size}" - ) + raise ValueError(f"target size should be tuple (height, width), instead got {target_size}") H, W = clip.size(-2), clip.size(-1) scale_ = target_size[0] / min(H, W) return torch.nn.functional.interpolate( @@ -174,8 +166,7 @@ def normalize_video(clip): """ _is_tensor_video_clip(clip) if not clip.dtype == torch.uint8: - raise TypeError("clip tensor should have data type uint8. Got %s" % - str(clip.dtype)) + raise TypeError("clip tensor should have data type uint8. Got %s" % str(clip.dtype)) # return clip.float().permute(3, 0, 1, 2) / 255.0 return clip.float() / 255.0 @@ -236,9 +227,7 @@ class RandomCropVideo: th, tw = self.size if h < th or w < tw: - raise ValueError( - f"Required crop size {(th, tw)} is larger than input image size {(h, w)}" - ) + raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}") if w == tw and h == th: return 0, 0, h, w @@ -312,9 +301,7 @@ class LongSideResizeVideo: else: h = int(h * self.size / w) w = self.size - resize_clip = resize(clip, - target_size=(h, w), - interpolation_mode=self.interpolation_mode) + resize_clip = resize(clip, target_size=(h, w), interpolation_mode=self.interpolation_mode) return resize_clip def __repr__(self) -> str: @@ -334,8 +321,7 @@ class CenterCropResizeVideo: interpolation_mode="bilinear", ): if len(size) != 2: - raise ValueError( - f"size should be tuple (height, width), instead got {size}") + raise ValueError(f"size should be tuple (height, width), instead got {size}") self.size = size self.top_crop = top_crop self.interpolation_mode = interpolation_mode @@ -349,10 +335,7 @@ class CenterCropResizeVideo: size is (T, C, crop_size, crop_size) """ # clip_center_crop = center_crop_using_short_edge(clip) - clip_center_crop = center_crop_th_tw(clip, - self.size[0], - self.size[1], - top_crop=self.top_crop) + clip_center_crop = center_crop_th_tw(clip, self.size[0], self.size[1], top_crop=self.top_crop) # import ipdb;ipdb.set_trace() clip_center_crop_resize = resize( clip_center_crop, @@ -378,9 +361,7 @@ class UCFCenterCropVideo: ): if isinstance(size, tuple): if len(size) != 2: - raise ValueError( - f"size should be tuple (height, width), instead got {size}" - ) + raise ValueError(f"size should be tuple (height, width), instead got {size}") self.size = size else: self.size = (size, size) @@ -395,9 +376,7 @@ class UCFCenterCropVideo: torch.tensor: scale resized / center cropped video clip. size is (T, C, crop_size, crop_size) """ - clip_resize = resize_scale(clip=clip, - target_size=self.size, - interpolation_mode=self.interpolation_mode) + clip_resize = resize_scale(clip=clip, target_size=self.size, interpolation_mode=self.interpolation_mode) clip_center_crop = center_crop(clip_resize, self.size) return clip_center_crop @@ -417,9 +396,7 @@ class KineticsRandomCropResizeVideo: ): if isinstance(size, tuple): if len(size) != 2: - raise ValueError( - f"size should be tuple (height, width), instead got {size}" - ) + raise ValueError(f"size should be tuple (height, width), instead got {size}") self.size = size else: self.size = (size, size) @@ -428,8 +405,7 @@ class KineticsRandomCropResizeVideo: def __call__(self, clip): clip_random_crop = random_shift_crop(clip) - clip_resize = resize(clip_random_crop, self.size, - self.interpolation_mode) + clip_resize = resize(clip_random_crop, self.size, self.interpolation_mode) return clip_resize @@ -442,9 +418,7 @@ class CenterCropVideo: ): if isinstance(size, tuple): if len(size) != 2: - raise ValueError( - f"size should be tuple (height, width), instead got {size}" - ) + raise ValueError(f"size should be tuple (height, width), instead got {size}") self.size = size else: self.size = (size, size) @@ -571,8 +545,7 @@ class DynamicSampleDuration(object): def __call__(self, t, h, w): if self.extra_1: t = t - 1 - truncate_t_list = list( - range(t + 1))[t // 2:][::self.t_stride] # need half at least + truncate_t_list = list(range(t + 1))[t // 2:][::self.t_stride] # need half at least truncate_t = random.choice(truncate_t_list) if self.extra_1: truncate_t = truncate_t + 1 @@ -587,18 +560,14 @@ if __name__ == "__main__": from torchvision import transforms from torchvision.utils import save_image - vframes, aframes, info = io.read_video(filename="./v_Archery_g01_c03.avi", - pts_unit="sec", - output_format="TCHW") + vframes, aframes, info = io.read_video(filename="./v_Archery_g01_c03.avi", pts_unit="sec", output_format="TCHW") trans = transforms.Compose([ Normalize255(), RandomHorizontalFlipVideo(), UCFCenterCropVideo(512), # NormalizeVideo(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), - transforms.Normalize(mean=[0.5, 0.5, 0.5], - std=[0.5, 0.5, 0.5], - inplace=True), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), ]) target_video_len = 32 @@ -613,10 +582,7 @@ if __name__ == "__main__": # print(start_frame_ind) # print(end_frame_ind) assert end_frame_ind - start_frame_ind >= target_video_len - frame_indice = np.linspace(start_frame_ind, - end_frame_ind - 1, - target_video_len, - dtype=int) + frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, target_video_len, dtype=int) print(frame_indice) select_vframes = vframes[frame_indice] @@ -627,14 +593,11 @@ if __name__ == "__main__": print(select_vframes_trans.shape) print(select_vframes_trans.dtype) - select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) * - 255).to(dtype=torch.uint8) + select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) * 255).to(dtype=torch.uint8) print(select_vframes_trans_int.dtype) print(select_vframes_trans_int.permute(0, 2, 3, 1).shape) - io.write_video("./test.avi", - select_vframes_trans_int.permute(0, 2, 3, 1), - fps=8) + io.write_video("./test.avi", select_vframes_trans_int.permute(0, 2, 3, 1), fps=8) for i in range(target_video_len): save_image( diff --git a/fastvideo/distill.py b/fastvideo/distill.py index f53469d77..488859ef9 100644 --- a/fastvideo/distill.py +++ b/fastvideo/distill.py @@ -21,23 +21,17 @@ from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from tqdm.auto import tqdm -from fastvideo.dataset.latent_datasets import (LatentDataset, - latent_collate_function) +from fastvideo.dataset.latent_datasets import (LatentDataset, latent_collate_function) from fastvideo.distill.solver import EulerSolver, extract_into_tensor from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input from fastvideo.models.mochi_hf.pipeline_mochi import linear_quadratic_schedule -from fastvideo.utils.checkpoint import (resume_lora_optimizer, save_checkpoint, - save_lora_checkpoint) -from fastvideo.utils.communications import (broadcast, - sp_parallel_dataloader_wrapper) +from fastvideo.utils.checkpoint import (resume_lora_optimizer, save_checkpoint, save_lora_checkpoint) +from fastvideo.utils.communications import (broadcast, sp_parallel_dataloader_wrapper) from fastvideo.utils.dataset_utils import LengthGroupedSampler -from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing, - get_dit_fsdp_kwargs) +from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing, get_dit_fsdp_kwargs) from fastvideo.utils.load import load_transformer -from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group, - get_sequence_parallel_state, - initialize_sequence_parallel_state - ) +from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group, get_sequence_parallel_state, + initialize_sequence_parallel_state) from fastvideo.utils.validation import log_validation # Will error if the minimal version of diffusers is not installed. Remove at your own risks. @@ -59,18 +53,14 @@ def get_norm(model_pred, norms, gradient_accumulation_steps): fro_norm = ( torch.linalg.matrix_norm(model_pred, ord="fro") / # codespell:ignore gradient_accumulation_steps) - largest_singular_value = (torch.linalg.matrix_norm(model_pred, ord=2) / - gradient_accumulation_steps) - absolute_mean = torch.mean( - torch.abs(model_pred)) / gradient_accumulation_steps - absolute_max = torch.max( - torch.abs(model_pred)) / gradient_accumulation_steps + largest_singular_value = (torch.linalg.matrix_norm(model_pred, ord=2) / gradient_accumulation_steps) + absolute_mean = torch.mean(torch.abs(model_pred)) / gradient_accumulation_steps + absolute_max = torch.max(torch.abs(model_pred)) / gradient_accumulation_steps dist.all_reduce(fro_norm, op=dist.ReduceOp.AVG) dist.all_reduce(largest_singular_value, op=dist.ReduceOp.AVG) dist.all_reduce(absolute_mean, op=dist.ReduceOp.AVG) norms["fro"] += torch.mean(fro_norm).item() # codespell:ignore - norms["largest singular value"] += torch.mean( - largest_singular_value).item() + norms["largest singular value"] += torch.mean(largest_singular_value).item() norms["absolute mean"] += absolute_mean.item() norms["absolute max"] += absolute_max.item() @@ -118,23 +108,18 @@ def distill_one_step( model_input = normalize_dit_input(model_type, latents) noise = torch.randn_like(model_input) bsz = model_input.shape[0] - index = torch.randint(0, - num_euler_timesteps, (bsz, ), - device=model_input.device).long() + index = torch.randint(0, num_euler_timesteps, (bsz, ), device=model_input.device).long() if sp_size > 1: broadcast(index) # Add noise according to flow matching. # sigmas = get_sigmas(start_timesteps, n_dim=model_input.ndim, dtype=model_input.dtype) sigmas = extract_into_tensor(solver.sigmas, index, model_input.shape) - sigmas_prev = extract_into_tensor(solver.sigmas_prev, index, - model_input.shape) + sigmas_prev = extract_into_tensor(solver.sigmas_prev, index, model_input.shape) - timesteps = (sigmas * - noise_scheduler.config.num_train_timesteps).view(-1) + timesteps = (sigmas * noise_scheduler.config.num_train_timesteps).view(-1) # if squeeze to [], unsqueeze to [1] - timesteps_prev = (sigmas_prev * - noise_scheduler.config.num_train_timesteps).view(-1) + timesteps_prev = (sigmas_prev * noise_scheduler.config.num_train_timesteps).view(-1) noisy_model_input = sigmas * noise + (1.0 - sigmas) * model_input # Predict the noise residual with torch.autocast("cuda", dtype=torch.bfloat16): @@ -146,15 +131,13 @@ def distill_one_step( "return_dict": False, } if hunyuan_teacher_disable_cfg: - teacher_kwargs["guidance"] = torch.tensor( - [1000.0], - device=noisy_model_input.device, - dtype=torch.bfloat16) + teacher_kwargs["guidance"] = torch.tensor([1000.0], + device=noisy_model_input.device, + dtype=torch.bfloat16) model_pred = transformer(**teacher_kwargs)[0] # if accelerator.is_main_process: - model_pred, end_index = solver.euler_style_multiphase_pred( - noisy_model_input, model_pred, index, multiphase) + model_pred, end_index = solver.euler_style_multiphase_pred(noisy_model_input, model_pred, index, multiphase) with torch.no_grad(): w = distill_cfg with torch.autocast("cuda", dtype=torch.bfloat16): @@ -177,10 +160,8 @@ def distill_one_step( uncond_prompt_mask.unsqueeze(0).expand(bsz, -1), return_dict=False, )[0].float() - teacher_output = cond_teacher_output + w * (cond_teacher_output - - uncond_teacher_output) - x_prev = solver.euler_step(noisy_model_input, teacher_output, - index) + teacher_output = cond_teacher_output + w * (cond_teacher_output - uncond_teacher_output) + x_prev = solver.euler_step(noisy_model_input, teacher_output, index) # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n with torch.no_grad(): @@ -202,33 +183,26 @@ def distill_one_step( return_dict=False, )[0] - target, end_index = solver.euler_style_multiphase_pred( - x_prev, target_pred, index, multiphase, True) + target, end_index = solver.euler_style_multiphase_pred(x_prev, target_pred, index, multiphase, True) huber_c = 0.001 # loss = loss.mean() - loss = (torch.mean( - torch.sqrt((model_pred.float() - target.float())**2 + huber_c**2) - - huber_c) / gradient_accumulation_steps) + loss = (torch.mean(torch.sqrt((model_pred.float() - target.float())**2 + huber_c**2) - huber_c) / + gradient_accumulation_steps) if pred_decay_weight > 0: if pred_decay_type == "l1": - pred_decay_loss = ( - torch.mean(torch.sqrt(model_pred.float()**2)) * - pred_decay_weight / gradient_accumulation_steps) + pred_decay_loss = (torch.mean(torch.sqrt(model_pred.float()**2)) * pred_decay_weight / + gradient_accumulation_steps) loss += pred_decay_loss elif pred_decay_type == "l2": # essnetially k2? - pred_decay_loss = (torch.mean(model_pred.float()**2) * - pred_decay_weight / - gradient_accumulation_steps) + pred_decay_loss = (torch.mean(model_pred.float()**2) * pred_decay_weight / gradient_accumulation_steps) loss += pred_decay_loss else: - assert NotImplementedError( - "pred_decay_type is not implemented") + assert NotImplementedError("pred_decay_type is not implemented") # calculate model_pred norm and mean - get_norm(model_pred.detach().float(), model_pred_norm, - gradient_accumulation_steps) + get_norm(model_pred.detach().float(), model_pred_norm, gradient_accumulation_steps) loss.backward() avg_loss = loss.detach().clone() @@ -238,12 +212,9 @@ def distill_one_step( # update ema if ema_transformer is not None: reshard_fsdp(ema_transformer) - for p_averaged, p_model in zip(ema_transformer.parameters(), - transformer.parameters()): + for p_averaged, p_model in zip(ema_transformer.parameters(), transformer.parameters()): with torch.no_grad(): - p_averaged.copy_( - torch.lerp(p_averaged.detach(), p_model.detach(), - 1 - ema_decay)) + p_averaged.copy_(torch.lerp(p_averaged.detach(), p_model.detach(), 1 - ema_decay)) grad_norm = transformer.clip_grad_norm_(max_grad_norm) optimizer.step() @@ -306,11 +277,8 @@ def main(args): transformer.add_adapter(transformer_lora_config) main_print( - f" Total training parameters = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e6} M" - ) - main_print( - f"--> Initializing FSDP with sharding strategy: {args.fsdp_sharding_startegy}" - ) + f" Total training parameters = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e6} M") + main_print(f"--> Initializing FSDP with sharding strategy: {args.fsdp_sharding_startegy}") fsdp_kwargs, no_split_modules = get_dit_fsdp_kwargs( transformer, args.fsdp_sharding_startegy, @@ -322,12 +290,9 @@ def main(args): if args.use_lora: transformer.config.lora_rank = args.lora_rank transformer.config.lora_alpha = args.lora_alpha - transformer.config.lora_target_modules = [ - "to_k", "to_q", "to_v", "to_out.0" - ] + transformer.config.lora_target_modules = ["to_k", "to_q", "to_v", "to_out.0"] transformer._no_split_modules = no_split_modules - fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"]( - transformer) + fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](transformer) transformer = FSDP( transformer, @@ -345,13 +310,10 @@ def main(args): main_print("--> model loaded") if args.gradient_checkpointing: - apply_fsdp_checkpointing(transformer, no_split_modules, - args.selective_checkpointing) - apply_fsdp_checkpointing(teacher_transformer, no_split_modules, - args.selective_checkpointing) + apply_fsdp_checkpointing(transformer, no_split_modules, args.selective_checkpointing) + apply_fsdp_checkpointing(teacher_transformer, no_split_modules, args.selective_checkpointing) if args.use_ema: - apply_fsdp_checkpointing(ema_transformer, no_split_modules, - args.selective_checkpointing) + apply_fsdp_checkpointing(ema_transformer, no_split_modules, args.selective_checkpointing) # Set model as trainable. transformer.train() teacher_transformer.requires_grad_(False) @@ -359,8 +321,7 @@ def main(args): ema_transformer.requires_grad_(False) noise_scheduler = FlowMatchEulerDiscreteScheduler(shift=args.shift) if args.scheduler_type == "pcm_linear_quadratic": - linear_steps = int(noise_scheduler.config.num_train_timesteps * - args.linear_range) + linear_steps = int(noise_scheduler.config.num_train_timesteps * args.linear_range) sigmas = linear_quadratic_schedule( noise_scheduler.config.num_train_timesteps, args.linear_quadratic_threshold, @@ -376,8 +337,7 @@ def main(args): ) solver.to(device) params_to_optimize = transformer.parameters() - params_to_optimize = list( - filter(lambda p: p.requires_grad, params_to_optimize)) + params_to_optimize = list(filter(lambda p: p.requires_grad, params_to_optimize)) optimizer = torch.optim.AdamW( params_to_optimize, @@ -389,8 +349,8 @@ def main(args): init_steps = 0 if args.resume_from_lora_checkpoint: - transformer, optimizer, init_steps = resume_lora_optimizer( - transformer, args.resume_from_lora_checkpoint, optimizer) + transformer, optimizer, init_steps = resume_lora_optimizer(transformer, args.resume_from_lora_checkpoint, + optimizer) main_print(f"optimizer: {optimizer}") # todo add lr scheduler @@ -404,8 +364,7 @@ def main(args): last_epoch=init_steps - 1, ) - train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, - args.cfg) + train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, args.cfg) uncond_prompt_embed = train_dataset.uncond_prompt_embed uncond_prompt_mask = train_dataset.uncond_prompt_mask sampler = (LengthGroupedSampler( @@ -429,42 +388,33 @@ def main(args): ) num_update_steps_per_epoch = math.ceil( - len(train_dataloader) / args.gradient_accumulation_steps * - args.sp_size / args.train_sp_batch_size) - args.num_train_epochs = math.ceil(args.max_train_steps / - num_update_steps_per_epoch) + len(train_dataloader) / args.gradient_accumulation_steps * args.sp_size / args.train_sp_batch_size) + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) if rank <= 0: project = args.tracker_project_name or "fastvideo" wandb.init(project=project, config=args) # Train! - total_batch_size = (world_size * args.gradient_accumulation_steps / - args.sp_size * args.train_sp_batch_size) + total_batch_size = (world_size * args.gradient_accumulation_steps / args.sp_size * args.train_sp_batch_size) main_print("***** Running training *****") main_print(f" Num examples = {len(train_dataset)}") main_print(f" Dataloader size = {len(train_dataloader)}") main_print(f" Num Epochs = {args.num_train_epochs}") main_print(f" Resume training from step {init_steps}") - main_print( - f" Instantaneous batch size per device = {args.train_batch_size}") - main_print( - f" Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}" - ) - main_print( - f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + main_print(f" Instantaneous batch size per device = {args.train_batch_size}") + main_print(f" Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}") + main_print(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") main_print(f" Total optimization steps = {args.max_train_steps}") main_print( f" Total training parameters per FSDP shard = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e9} B" ) # print dtype - main_print( - f" Master weight dtype: {transformer.parameters().__next__().dtype}") + main_print(f" Master weight dtype: {transformer.parameters().__next__().dtype}") # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: - assert NotImplementedError( - "resume_from_checkpoint is not supported now.") + assert NotImplementedError("resume_from_checkpoint is not supported now.") # TODO progress_bar = tqdm( @@ -546,37 +496,26 @@ def main(args): if rank <= 0: wandb.log( { - "train_loss": - loss, - "learning_rate": - lr_scheduler.get_last_lr()[0], - "step_time": - step_time, - "avg_step_time": - avg_step_time, - "grad_norm": - grad_norm, - "pred_fro_norm": - pred_norm["fro"], # codespell:ignore - "pred_largest_singular_value": - pred_norm["largest singular value"], - "pred_absolute_mean": - pred_norm["absolute mean"], - "pred_absolute_max": - pred_norm["absolute max"], + "train_loss": loss, + "learning_rate": lr_scheduler.get_last_lr()[0], + "step_time": step_time, + "avg_step_time": avg_step_time, + "grad_norm": grad_norm, + "pred_fro_norm": pred_norm["fro"], # codespell:ignore + "pred_largest_singular_value": pred_norm["largest singular value"], + "pred_absolute_mean": pred_norm["absolute mean"], + "pred_absolute_max": pred_norm["absolute max"], }, step=step, ) if step % args.checkpointing_steps == 0: if args.use_lora: # Save LoRA weights - save_lora_checkpoint(transformer, optimizer, rank, - args.output_dir, step) + save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, step) else: # Your existing checkpoint saving code if args.use_ema: - save_checkpoint(ema_transformer, rank, args.output_dir, - step) + save_checkpoint(ema_transformer, rank, args.output_dir, step) else: save_checkpoint(transformer, rank, args.output_dir, step) dist.barrier() @@ -610,11 +549,9 @@ def main(args): ) if args.use_lora: - save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, - args.max_train_steps) + save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, args.max_train_steps) else: - save_checkpoint(transformer, rank, args.output_dir, - args.max_train_steps) + save_checkpoint(transformer, rank, args.output_dir, args.max_train_steps) if get_sequence_parallel_state(): destroy_sequence_parallel_group() @@ -623,10 +560,7 @@ def main(args): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument("--model_type", - type=str, - default="mochi", - help="The type of model to train.") + parser.add_argument("--model_type", type=str, default="mochi", help="The type of model to train.") # dataset & dataloader parser.add_argument("--data_json_path", type=str, required=True) @@ -637,8 +571,7 @@ if __name__ == "__main__": "--dataloader_num_workers", type=int, default=10, - help= - "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--train_batch_size", @@ -646,10 +579,7 @@ if __name__ == "__main__": default=16, help="Batch size (per device) for the training dataloader.", ) - parser.add_argument("--num_latent_t", - type=int, - default=28, - help="Number of latent timesteps.") + parser.add_argument("--num_latent_t", type=int, default=28, help="Number of latent timesteps.") parser.add_argument("--group_frame", action="store_true") # TODO parser.add_argument("--group_resolution", action="store_true") # TODO @@ -671,16 +601,12 @@ if __name__ == "__main__": parser.add_argument("--validation_steps", type=float, default=64) parser.add_argument("--log_validation", action="store_true") parser.add_argument("--tracker_project_name", type=str, default=None) - parser.add_argument("--seed", - type=int, - default=None, - help="A seed for reproducible training.") + parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--output_dir", type=str, default=None, - help= - "The output directory where the model predictions and checkpoints will be written.", + help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--checkpoints_total_limit", @@ -692,37 +618,31 @@ if __name__ == "__main__": "--checkpointing_steps", type=int, default=500, - help= - ("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" - " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" - " training using `--resume_from_checkpoint`."), + help=("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" + " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" + " training using `--resume_from_checkpoint`."), ) parser.add_argument("--shift", type=float, default=1.0) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, - help= - ("Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), + help=("Whether training should be resumed from a previous checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'), ) parser.add_argument( "--resume_from_lora_checkpoint", type=str, default=None, - help= - ("Whether training should be resumed from a previous lora checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), + help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'), ) parser.add_argument( "--logging_dir", type=str, default="logs", - help= - ("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), + help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), ) # optimizer & scheduler & Training @@ -731,29 +651,25 @@ if __name__ == "__main__": "--max_train_steps", type=int, default=None, - help= - "Total number of training steps to perform. If provided, overrides num_train_epochs.", + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, - help= - "Number of updates steps to accumulate before performing a backward/update pass.", + help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, - help= - "Initial learning rate (after the potential warmup period) to use.", + help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, - help= - "Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_warmup_steps", @@ -761,47 +677,36 @@ if __name__ == "__main__": default=10, help="Number of steps for the warmup in the lr scheduler.", ) - parser.add_argument("--max_grad_norm", - default=1.0, - type=float, - help="Max gradient norm.") + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--gradient_checkpointing", action="store_true", - help= - "Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument("--selective_checkpointing", type=float, default=1.0) parser.add_argument( "--allow_tf32", action="store_true", - help= - ("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" - " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" - ), + help=("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" + " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], - help= - ("Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), + help=( + "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" + " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."), ) parser.add_argument( "--use_cpu_offload", action="store_true", - help= - "Whether to use CPU offload for param & gradient & optimizer states.", + help="Whether to use CPU offload for param & gradient & optimizer states.", ) - parser.add_argument("--sp_size", - type=int, - default=1, - help="For sequence parallel") + parser.add_argument("--sp_size", type=int, default=1, help="For sequence parallel") parser.add_argument( "--train_sp_batch_size", type=int, @@ -815,14 +720,8 @@ if __name__ == "__main__": default=False, help="Whether to use LoRA for finetuning.", ) - parser.add_argument("--lora_alpha", - type=int, - default=256, - help="Alpha parameter for LoRA.") - parser.add_argument("--lora_rank", - type=int, - default=128, - help="LoRA rank parameter. ") + parser.add_argument("--lora_alpha", type=int, default=256, help="Alpha parameter for LoRA.") + parser.add_argument("--lora_rank", type=int, default=128, help="LoRA rank parameter. ") parser.add_argument("--fsdp_sharding_startegy", default="full") # lr_scheduler @@ -830,9 +729,8 @@ if __name__ == "__main__": "--lr_scheduler", type=str, default="constant", - help= - ('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]'), + help=('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]'), ) parser.add_argument("--num_euler_timesteps", type=int, default=100) parser.add_argument( @@ -852,15 +750,9 @@ if __name__ == "__main__": action="store_true", help="Whether to apply the cfg_solver.", ) - parser.add_argument("--distill_cfg", - type=float, - default=3.0, - help="Distillation coefficient.") + parser.add_argument("--distill_cfg", type=float, default=3.0, help="Distillation coefficient.") # ["euler_linear_quadratic", "pcm", "pcm_linear_qudratic"] - parser.add_argument("--scheduler_type", - type=str, - default="pcm", - help="The scheduler type to use.") + parser.add_argument("--scheduler_type", type=str, default="pcm", help="The scheduler type to use.") parser.add_argument( "--linear_quadratic_threshold", type=float, @@ -873,16 +765,9 @@ if __name__ == "__main__": default=0.5, help="Range for linear quadratic scheduler.", ) - parser.add_argument("--weight_decay", - type=float, - default=0.001, - help="Weight decay to apply.") - parser.add_argument("--use_ema", - action="store_true", - help="Whether to use EMA.") - parser.add_argument("--multi_phased_distill_schedule", - type=str, - default=None) + parser.add_argument("--weight_decay", type=float, default=0.001, help="Weight decay to apply.") + parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA.") + parser.add_argument("--multi_phased_distill_schedule", type=str, default=None) parser.add_argument("--pred_decay_weight", type=float, default=0.0) parser.add_argument("--pred_decay_type", default="l1") parser.add_argument("--hunyuan_teacher_disable_cfg", action="store_true") diff --git a/fastvideo/distill/discriminator.py b/fastvideo/distill/discriminator.py index 2ea7264b6..8185e3325 100644 --- a/fastvideo/distill/discriminator.py +++ b/fastvideo/distill/discriminator.py @@ -12,16 +12,12 @@ class DiscriminatorHead(nn.Module): self.conv1 = nn.Sequential( nn.Conv2d(input_channel, inner_channel, 1, 1, 0), nn.GroupNorm(32, inner_channel), - nn.LeakyReLU( - inplace=True - ), # use LeakyReLu instead of GELU shown in the paper to save memory + nn.LeakyReLU(inplace=True), # use LeakyReLu instead of GELU shown in the paper to save memory ) self.conv2 = nn.Sequential( nn.Conv2d(inner_channel, inner_channel, 1, 1, 0), nn.GroupNorm(32, inner_channel), - nn.LeakyReLU( - inplace=True - ), # use LeakyReLu instead of GELU shown in the paper to save memory + nn.LeakyReLU(inplace=True), # use LeakyReLu instead of GELU shown in the paper to save memory ) self.conv_out = nn.Conv2d(inner_channel, output_channel, 1, 1, 0) @@ -53,10 +49,8 @@ class Discriminator(nn.Module): self.num_h_per_head = num_h_per_head self.head_num = len(adapter_channel_dims) self.heads = nn.ModuleList([ - nn.ModuleList([ - DiscriminatorHead(adapter_channel) - for _ in range(self.num_h_per_head) - ]) for adapter_channel in adapter_channel_dims + nn.ModuleList([DiscriminatorHead(adapter_channel) for _ in range(self.num_h_per_head)]) + for adapter_channel in adapter_channel_dims ]) def forward(self, features): diff --git a/fastvideo/distill/solver.py b/fastvideo/distill/solver.py index 1b960c9f4..7f8fec847 100644 --- a/fastvideo/distill/solver.py +++ b/fastvideo/distill/solver.py @@ -39,28 +39,21 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin): ): if linear_quadratic: linear_steps = int(num_train_timesteps * linear_range) - sigmas = linear_quadratic_schedule(num_train_timesteps, - linear_quadratic_threshold, - linear_steps) + sigmas = linear_quadratic_schedule(num_train_timesteps, linear_quadratic_threshold, linear_steps) sigmas = torch.tensor(sigmas).to(dtype=torch.float32) else: - timesteps = np.linspace(1, - num_train_timesteps, - num_train_timesteps, - dtype=np.float32)[::-1].copy() + timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy() timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) sigmas = timesteps / num_train_timesteps sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) self.euler_timesteps = (np.arange(1, pcm_timesteps + 1) * - (num_train_timesteps // - pcm_timesteps)).round().astype(np.int64) - 1 + (num_train_timesteps // pcm_timesteps)).round().astype(np.int64) - 1 self.sigmas = sigmas.numpy()[::-1][self.euler_timesteps] self.sigmas = torch.from_numpy((self.sigmas[::-1].copy())) self.timesteps = self.sigmas * num_train_timesteps self._step_index = None self._begin_index = None - self.sigmas = self.sigmas.to( - "cpu") # to avoid too much CPU/GPU communication + self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication self.sigma_min = self.sigmas[-1].item() self.sigma_max = self.sigmas[0].item() @@ -119,9 +112,7 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin): def _sigma_to_t(self, sigma): return sigma * self.config.num_train_timesteps - def set_timesteps(self, - num_inference_steps: int, - device: Union[str, torch.device] = None): + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the discrete timesteps used for the diffusion chain (to be run before inference). @@ -132,19 +123,14 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps - inference_indices = np.linspace(0, - self.config.pcm_timesteps, - num=num_inference_steps, - endpoint=False) + inference_indices = np.linspace(0, self.config.pcm_timesteps, num=num_inference_steps, endpoint=False) inference_indices = np.floor(inference_indices).astype(np.int64) inference_indices = torch.from_numpy(inference_indices).long() self.sigmas_ = self.sigmas[inference_indices] timesteps = self.sigmas_ * self.config.num_train_timesteps self.timesteps = timesteps.to(device=device) - self.sigmas_ = torch.cat( - [self.sigmas_, - torch.zeros(1, device=self.sigmas_.device)]) + self.sigmas_ = torch.cat([self.sigmas_, torch.zeros(1, device=self.sigmas_.device)]) self._step_index = None self._begin_index = None @@ -208,10 +194,9 @@ class PCMFMScheduler(SchedulerMixin, ConfigMixin): if (isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) or isinstance(timestep, torch.LongTensor)): - raise ValueError(( - "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" - " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" - " one of the `scheduler.timesteps` as a timestep."), ) + raise ValueError(("Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep."), ) if self.step_index is None: self._init_step_index(timestep) @@ -241,18 +226,14 @@ class EulerSolver: def __init__(self, sigmas, timesteps=1000, euler_timesteps=50): self.step_ratio = timesteps // euler_timesteps - self.euler_timesteps = (np.arange(1, euler_timesteps + 1) * - self.step_ratio).round().astype(np.int64) - 1 - self.euler_timesteps_prev = np.asarray( - [0] + self.euler_timesteps[:-1].tolist()) + self.euler_timesteps = (np.arange(1, euler_timesteps + 1) * self.step_ratio).round().astype(np.int64) - 1 + self.euler_timesteps_prev = np.asarray([0] + self.euler_timesteps[:-1].tolist()) self.sigmas = sigmas[self.euler_timesteps] - self.sigmas_prev = np.asarray( - [sigmas[0]] + sigmas[self.euler_timesteps[:-1]].tolist() - ) # either use sigma0 or 0 + self.sigmas_prev = np.asarray([sigmas[0]] + + sigmas[self.euler_timesteps[:-1]].tolist()) # either use sigma0 or 0 self.euler_timesteps = torch.from_numpy(self.euler_timesteps).long() - self.euler_timesteps_prev = torch.from_numpy( - self.euler_timesteps_prev).long() + self.euler_timesteps_prev = torch.from_numpy(self.euler_timesteps_prev).long() self.sigmas = torch.from_numpy(self.sigmas) self.sigmas_prev = torch.from_numpy(self.sigmas_prev) @@ -265,10 +246,8 @@ class EulerSolver: return self def euler_step(self, sample, model_pred, timestep_index): - sigma = extract_into_tensor(self.sigmas, timestep_index, - model_pred.shape) - sigma_prev = extract_into_tensor(self.sigmas_prev, timestep_index, - model_pred.shape) + sigma = extract_into_tensor(self.sigmas, timestep_index, model_pred.shape) + sigma_prev = extract_into_tensor(self.sigmas_prev, timestep_index, model_pred.shape) x_prev = sample + (sigma_prev - sigma) * model_pred return x_prev @@ -280,29 +259,20 @@ class EulerSolver: multiphase, is_target=False, ): - inference_indices = np.linspace(0, - len(self.euler_timesteps), - num=multiphase, - endpoint=False) + inference_indices = np.linspace(0, len(self.euler_timesteps), num=multiphase, endpoint=False) inference_indices = np.floor(inference_indices).astype(np.int64) - inference_indices = (torch.from_numpy(inference_indices).long().to( - self.euler_timesteps.device)) - expanded_timestep_index = timestep_index.unsqueeze(1).expand( - -1, inference_indices.size(0)) + inference_indices = (torch.from_numpy(inference_indices).long().to(self.euler_timesteps.device)) + expanded_timestep_index = timestep_index.unsqueeze(1).expand(-1, inference_indices.size(0)) valid_indices_mask = expanded_timestep_index >= inference_indices - last_valid_index = valid_indices_mask.flip(dims=[1]).long().argmax( - dim=1) + last_valid_index = valid_indices_mask.flip(dims=[1]).long().argmax(dim=1) last_valid_index = inference_indices.size(0) - 1 - last_valid_index timestep_index_end = inference_indices[last_valid_index] if is_target: - sigma = extract_into_tensor(self.sigmas_prev, timestep_index, - sample.shape) + sigma = extract_into_tensor(self.sigmas_prev, timestep_index, sample.shape) else: - sigma = extract_into_tensor(self.sigmas, timestep_index, - sample.shape) - sigma_prev = extract_into_tensor(self.sigmas_prev, timestep_index_end, - sample.shape) + sigma = extract_into_tensor(self.sigmas, timestep_index, sample.shape) + sigma_prev = extract_into_tensor(self.sigmas_prev, timestep_index_end, sample.shape) x_prev = sample + (sigma_prev - sigma) * model_pred return x_prev, timestep_index_end diff --git a/fastvideo/distill_adv.py b/fastvideo/distill_adv.py index 294f3845d..e2a4716c5 100644 --- a/fastvideo/distill_adv.py +++ b/fastvideo/distill_adv.py @@ -20,27 +20,20 @@ from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from tqdm.auto import tqdm -from fastvideo.dataset.latent_datasets import (LatentDataset, - latent_collate_function) +from fastvideo.dataset.latent_datasets import (LatentDataset, latent_collate_function) from fastvideo.distill.discriminator import Discriminator from fastvideo.distill.solver import EulerSolver, extract_into_tensor from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input from fastvideo.models.mochi_hf.pipeline_mochi import linear_quadratic_schedule -from fastvideo.utils.checkpoint import ( - resume_lora_optimizer, resume_training_generator_discriminator, - save_checkpoint, save_lora_checkpoint) -from fastvideo.utils.communications import (broadcast, - sp_parallel_dataloader_wrapper) +from fastvideo.utils.checkpoint import (resume_lora_optimizer, resume_training_generator_discriminator, save_checkpoint, + save_lora_checkpoint) +from fastvideo.utils.communications import (broadcast, sp_parallel_dataloader_wrapper) from fastvideo.utils.dataset_utils import LengthGroupedSampler -from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing, - get_discriminator_fsdp_kwargs, - get_dit_fsdp_kwargs) +from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing, get_discriminator_fsdp_kwargs, get_dit_fsdp_kwargs) from fastvideo.utils.load import load_transformer from fastvideo.utils.logging_ import main_print -from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group, - get_sequence_parallel_state, - initialize_sequence_parallel_state - ) +from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group, get_sequence_parallel_state, + initialize_sequence_parallel_state) from fastvideo.utils.validation import log_validation # Will error if the minimal version of diffusers is not installed. Remove at your own risks. @@ -83,9 +76,8 @@ def gan_d_loss( fake_outputs = discriminator(fake_features) real_outputs = discriminator(real_features) for fake_output, real_output in zip(fake_outputs, real_outputs): - loss += (torch.mean(weight * torch.relu(fake_output.float() + 1)) + - torch.mean(weight * torch.relu(1 - real_output.float()))) / ( - discriminator.head_num * discriminator.num_h_per_head) + loss += (torch.mean(weight * torch.relu(fake_output.float() + 1)) + torch.mean( + weight * torch.relu(1 - real_output.float()))) / (discriminator.head_num * discriminator.num_h_per_head) return loss @@ -111,8 +103,8 @@ def gan_g_loss( )[1] fake_outputs = discriminator(features, ) for fake_output in fake_outputs: - loss += torch.mean(weight * torch.relu(1 - fake_output.float())) / ( - discriminator.head_num * discriminator.num_h_per_head) + loss += torch.mean( + weight * torch.relu(1 - fake_output.float())) / (discriminator.head_num * discriminator.num_h_per_head) return loss @@ -151,22 +143,18 @@ def distill_one_step_adv( model_input = normalize_dit_input(model_type, latents) noise = torch.randn_like(model_input) bsz = model_input.shape[0] - index = torch.randint(0, - num_euler_timesteps, (bsz, ), - device=model_input.device).long() + index = torch.randint(0, num_euler_timesteps, (bsz, ), device=model_input.device).long() if sp_size > 1: broadcast(index) # Add noise according to flow matching. # sigmas = get_sigmas(start_timesteps, n_dim=model_input.ndim, dtype=model_input.dtype) sigmas = extract_into_tensor(solver.sigmas, index, model_input.shape) - sigmas_prev = extract_into_tensor(solver.sigmas_prev, index, - model_input.shape) + sigmas_prev = extract_into_tensor(solver.sigmas_prev, index, model_input.shape) timesteps = (sigmas * noise_scheduler.config.num_train_timesteps).view(-1) # if squeeze to [], unsqueeze to [1] - timesteps_prev = (sigmas_prev * - noise_scheduler.config.num_train_timesteps).view(-1) + timesteps_prev = (sigmas_prev * noise_scheduler.config.num_train_timesteps).view(-1) noisy_model_input = sigmas * noise + (1.0 - sigmas) * model_input # Predict the noise residual @@ -180,8 +168,7 @@ def distill_one_step_adv( )[0] # if accelerator.is_main_process: - model_pred, end_index = solver.euler_style_multiphase_pred( - noisy_model_input, model_pred, index, multiphase) + model_pred, end_index = solver.euler_style_multiphase_pred(noisy_model_input, model_pred, index, multiphase) # # simplified flow matching aka 0-rectified flow matching loss # # target = model_input - noise @@ -196,12 +183,9 @@ def distill_one_step_adv( device=end_index.device, ) - sigmas_end = extract_into_tensor(solver.sigmas_prev, end_index, - model_input.shape) - sigmas_adv = extract_into_tensor(solver.sigmas_prev, adv_index, - model_input.shape) - timesteps_adv = (sigmas_adv * - noise_scheduler.config.num_train_timesteps).view(-1) + sigmas_end = extract_into_tensor(solver.sigmas_prev, end_index, model_input.shape) + sigmas_adv = extract_into_tensor(solver.sigmas_prev, adv_index, model_input.shape) + timesteps_adv = (sigmas_adv * noise_scheduler.config.num_train_timesteps).view(-1) with torch.no_grad(): w = distill_cfg @@ -225,8 +209,7 @@ def distill_one_step_adv( uncond_prompt_mask.unsqueeze(0).expand(bsz, -1), return_dict=False, )[0].float() - teacher_output = cond_teacher_output + w * (cond_teacher_output - - uncond_teacher_output) + teacher_output = cond_teacher_output + w * (cond_teacher_output - uncond_teacher_output) x_prev = solver.euler_step(noisy_model_input, teacher_output, index) # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n @@ -240,20 +223,14 @@ def distill_one_step_adv( return_dict=False, )[0] - target, end_index = solver.euler_style_multiphase_pred( - x_prev, target_pred, index, multiphase, True) + target, end_index = solver.euler_style_multiphase_pred(x_prev, target_pred, index, multiphase, True) - real_adv = ((1 - sigmas_adv) * target + - (sigmas_adv - sigmas_end) * torch.randn_like(target)) / ( - 1 - sigmas_end) + real_adv = ((1 - sigmas_adv) * target + (sigmas_adv - sigmas_end) * torch.randn_like(target)) / (1 - sigmas_end) fake_adv = ((1 - sigmas_adv) * model_pred + - (sigmas_adv - sigmas_end) * torch.randn_like(model_pred)) / ( - 1 - sigmas_end) + (sigmas_adv - sigmas_end) * torch.randn_like(model_pred)) / (1 - sigmas_end) huber_c = 0.001 - g_loss = torch.mean( - torch.sqrt((model_pred.float() - target.float())**2 + huber_c**2) - - huber_c) + g_loss = torch.mean(torch.sqrt((model_pred.float() - target.float())**2 + huber_c**2) - huber_c) discriminator.requires_grad_(False) with torch.autocast("cuda", dtype=torch.bfloat16): g_gan_loss = adv_weight * gan_g_loss( @@ -358,9 +335,7 @@ def main(args): main_print( f" Total discriminator parameters = {sum(p.numel() for p in discriminator.parameters() if p.requires_grad) / 1e6} M" ) - main_print( - f"--> Initializing FSDP with sharding strategy: {args.fsdp_sharding_startegy}" - ) + main_print(f"--> Initializing FSDP with sharding strategy: {args.fsdp_sharding_startegy}") fsdp_kwargs, no_split_modules = get_dit_fsdp_kwargs( transformer, args.fsdp_sharding_startegy, @@ -368,18 +343,14 @@ def main(args): args.use_cpu_offload, args.master_weight_type, ) - discriminator_fsdp_kwargs = get_discriminator_fsdp_kwargs( - args.master_weight_type) + discriminator_fsdp_kwargs = get_discriminator_fsdp_kwargs(args.master_weight_type) if args.use_lora: assert args.model_type == "mochi", "LoRA is only supported for Mochi model." transformer.config.lora_rank = args.lora_rank transformer.config.lora_alpha = args.lora_alpha - transformer.config.lora_target_modules = [ - "to_k", "to_q", "to_v", "to_out.0" - ] + transformer.config.lora_target_modules = ["to_k", "to_q", "to_v", "to_out.0"] transformer._no_split_modules = no_split_modules - fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"]( - transformer) + fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](transformer) transformer = FSDP( transformer, @@ -396,18 +367,14 @@ def main(args): main_print("--> model loaded") if args.gradient_checkpointing: - apply_fsdp_checkpointing(transformer, no_split_modules, - args.selective_checkpointing) - apply_fsdp_checkpointing(teacher_transformer, no_split_modules, - args.selective_checkpointing) + apply_fsdp_checkpointing(transformer, no_split_modules, args.selective_checkpointing) + apply_fsdp_checkpointing(teacher_transformer, no_split_modules, args.selective_checkpointing) # Set model as trainable. transformer.train() teacher_transformer.requires_grad_(False) noise_scheduler = FlowMatchEulerDiscreteScheduler(shift=args.shift) if args.scheduler_type == "pcm_linear_quadratic": - sigmas = linear_quadratic_schedule( - noise_scheduler.config.num_train_timesteps, - args.linear_quadratic_threshold) + sigmas = linear_quadratic_schedule(noise_scheduler.config.num_train_timesteps, args.linear_quadratic_threshold) sigmas = torch.tensor(sigmas).to(dtype=torch.float32) else: sigmas = noise_scheduler.sigmas @@ -418,8 +385,7 @@ def main(args): ) solver.to(device) params_to_optimize = transformer.parameters() - params_to_optimize = list( - filter(lambda p: p.requires_grad, params_to_optimize)) + params_to_optimize = list(filter(lambda p: p.requires_grad, params_to_optimize)) optimizer = torch.optim.AdamW( params_to_optimize, @@ -439,8 +405,8 @@ def main(args): init_steps = 0 if args.resume_from_lora_checkpoint: - transformer, optimizer, init_steps = resume_lora_optimizer( - transformer, args.resume_from_lora_checkpoint, optimizer) + transformer, optimizer, init_steps = resume_lora_optimizer(transformer, args.resume_from_lora_checkpoint, + optimizer) elif args.resume_from_checkpoint: ( transformer, @@ -469,8 +435,7 @@ def main(args): last_epoch=init_steps - 1, ) - train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, - args.cfg) + train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, args.cfg) uncond_prompt_embed = train_dataset.uncond_prompt_embed uncond_prompt_mask = train_dataset.uncond_prompt_mask sampler = (LengthGroupedSampler( @@ -494,37 +459,29 @@ def main(args): ) assert args.gradient_accumulation_steps == 1 num_update_steps_per_epoch = math.ceil( - len(train_dataloader) / args.gradient_accumulation_steps * - args.sp_size / args.train_sp_batch_size) - args.num_train_epochs = math.ceil(args.max_train_steps / - num_update_steps_per_epoch) + len(train_dataloader) / args.gradient_accumulation_steps * args.sp_size / args.train_sp_batch_size) + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) if rank <= 0: project = args.tracker_project_name or "fastvideo" wandb.init(project=project, config=args) # Train! - total_batch_size = (world_size * args.gradient_accumulation_steps / - args.sp_size * args.train_sp_batch_size) + total_batch_size = (world_size * args.gradient_accumulation_steps / args.sp_size * args.train_sp_batch_size) main_print("***** Running training *****") main_print(f" Num examples = {len(train_dataset)}") main_print(f" Dataloader size = {len(train_dataloader)}") main_print(f" Num Epochs = {args.num_train_epochs}") main_print(f" Resume training from step {init_steps}") - main_print( - f" Instantaneous batch size per device = {args.train_batch_size}") - main_print( - f" Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}" - ) - main_print( - f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + main_print(f" Instantaneous batch size per device = {args.train_batch_size}") + main_print(f" Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}") + main_print(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") main_print(f" Total optimization steps = {args.max_train_steps}") main_print( f" Total training parameters per FSDP shard = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e9} B" ) # print dtype - main_print( - f" Master weight dtype: {transformer.parameters().__next__().dtype}") + main_print(f" Master weight dtype: {transformer.parameters().__next__().dtype}") progress_bar = tqdm( range(0, args.max_train_steps), @@ -619,8 +576,7 @@ def main(args): main_print(f"--> saving checkpoint at step {step}") if args.use_lora: # Save LoRA weights - save_lora_checkpoint(transformer, optimizer, rank, - args.output_dir, step) + save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, step) else: # Your existing checkpoint saving code # TODO @@ -652,11 +608,9 @@ def main(args): ) if args.use_lora: - save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, - args.max_train_steps) + save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, args.max_train_steps) else: - save_checkpoint(transformer, rank, args.output_dir, - args.max_train_steps) + save_checkpoint(transformer, rank, args.output_dir, args.max_train_steps) if get_sequence_parallel_state(): destroy_sequence_parallel_group() @@ -665,10 +619,7 @@ def main(args): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument("--model_type", - type=str, - default="mochi", - help="The type of model to train.") + parser.add_argument("--model_type", type=str, default="mochi", help="The type of model to train.") # dataset & dataloader parser.add_argument("--data_json_path", type=str, required=True) parser.add_argument("--num_height", type=int, default=480) @@ -678,8 +629,7 @@ if __name__ == "__main__": "--dataloader_num_workers", type=int, default=10, - help= - "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--train_batch_size", @@ -687,10 +637,7 @@ if __name__ == "__main__": default=16, help="Batch size (per device) for the training dataloader.", ) - parser.add_argument("--num_latent_t", - type=int, - default=28, - help="Number of latent timesteps.") + parser.add_argument("--num_latent_t", type=int, default=28, help="Number of latent timesteps.") parser.add_argument("--group_frame", action="store_true") # TODO parser.add_argument("--group_resolution", action="store_true") # TODO @@ -709,16 +656,12 @@ if __name__ == "__main__": parser.add_argument("--validation_steps", type=float, default=64) parser.add_argument("--log_validation", action="store_true") parser.add_argument("--tracker_project_name", type=str, default=None) - parser.add_argument("--seed", - type=int, - default=None, - help="A seed for reproducible training.") + parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--output_dir", type=str, default=None, - help= - "The output directory where the model predictions and checkpoints will be written.", + help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--checkpoints_total_limit", @@ -730,10 +673,9 @@ if __name__ == "__main__": "--checkpointing_steps", type=int, default=500, - help= - ("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" - " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" - " training using `--resume_from_checkpoint`."), + help=("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" + " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" + " training using `--resume_from_checkpoint`."), ) parser.add_argument("--validation_prompt_dir", type=str) parser.add_argument("--shift", type=float, default=1.0) @@ -741,27 +683,22 @@ if __name__ == "__main__": "--resume_from_checkpoint", type=str, default=None, - help= - ("Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), + help=("Whether training should be resumed from a previous checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'), ) parser.add_argument( "--resume_from_lora_checkpoint", type=str, default=None, - help= - ("Whether training should be resumed from a previous lora checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), + help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'), ) parser.add_argument( "--logging_dir", type=str, default="logs", - help= - ("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), + help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), ) # optimizer & scheduler & Training @@ -770,29 +707,25 @@ if __name__ == "__main__": "--max_train_steps", type=int, default=None, - help= - "Total number of training steps to perform. If provided, overrides num_train_epochs.", + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, - help= - "Initial learning rate (after the potential warmup period) to use.", + help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--discriminator_learning_rate", type=float, default=1e-5, - help= - "Initial learning rate (after the potential warmup period) to use.", + help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, - help= - "Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_warmup_steps", @@ -800,47 +733,36 @@ if __name__ == "__main__": default=10, help="Number of steps for the warmup in the lr scheduler.", ) - parser.add_argument("--max_grad_norm", - default=1.0, - type=float, - help="Max gradient norm.") + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--gradient_checkpointing", action="store_true", - help= - "Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument("--selective_checkpointing", type=float, default=1.0) parser.add_argument( "--allow_tf32", action="store_true", - help= - ("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" - " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" - ), + help=("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" + " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], - help= - ("Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), + help=( + "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" + " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."), ) parser.add_argument( "--use_cpu_offload", action="store_true", - help= - "Whether to use CPU offload for param & gradient & optimizer states.", + help="Whether to use CPU offload for param & gradient & optimizer states.", ) - parser.add_argument("--sp_size", - type=int, - default=1, - help="For sequence parallel") + parser.add_argument("--sp_size", type=int, default=1, help="For sequence parallel") parser.add_argument( "--train_sp_batch_size", type=int, @@ -854,24 +776,15 @@ if __name__ == "__main__": default=False, help="Whether to use LoRA for finetuning.", ) - parser.add_argument("--lora_alpha", - type=int, - default=256, - help="Alpha parameter for LoRA.") - parser.add_argument("--lora_rank", - type=int, - default=128, - help="LoRA rank parameter. ") + parser.add_argument("--lora_alpha", type=int, default=256, help="Alpha parameter for LoRA.") + parser.add_argument("--lora_rank", type=int, default=128, help="LoRA rank parameter. ") parser.add_argument("--fsdp_sharding_startegy", default="full") - parser.add_argument("--multi_phased_distill_schedule", - type=str, - default=None) + parser.add_argument("--multi_phased_distill_schedule", type=str, default=None) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, - help= - "Number of updates steps to accumulate before performing a backward/update pass.", + help="Number of updates steps to accumulate before performing a backward/update pass.", ) # lr_scheduler @@ -879,9 +792,8 @@ if __name__ == "__main__": "--lr_scheduler", type=str, default="constant", - help= - ('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]'), + help=('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]'), ) parser.add_argument("--num_euler_timesteps", type=int, default=100) parser.add_argument( @@ -901,15 +813,9 @@ if __name__ == "__main__": action="store_true", help="Whether to apply the cfg_solver.", ) - parser.add_argument("--distill_cfg", - type=float, - default=3.0, - help="Distillation coefficient.") + parser.add_argument("--distill_cfg", type=float, default=3.0, help="Distillation coefficient.") # ["euler_linear_quadratic", "pcm", "pcm_linear_qudratic"] - parser.add_argument("--scheduler_type", - type=str, - default="pcm", - help="The scheduler type to use.") + parser.add_argument("--scheduler_type", type=str, default="pcm", help="The scheduler type to use.") parser.add_argument( "--adv_weight", type=float, @@ -928,10 +834,7 @@ if __name__ == "__main__": default=0.5, help="Range for linear quadratic scheduler.", ) - parser.add_argument("--weight_decay", - type=float, - default=0.001, - help="Weight decay to apply.") + parser.add_argument("--weight_decay", type=float, default=0.001, help="Weight decay to apply.") parser.add_argument( "--linear_quadratic_threshold", type=float, diff --git a/fastvideo/models/flash_attn_no_pad.py b/fastvideo/models/flash_attn_no_pad.py index 3bffc6947..f686e70a6 100644 --- a/fastvideo/models/flash_attn_no_pad.py +++ b/fastvideo/models/flash_attn_no_pad.py @@ -3,23 +3,15 @@ from flash_attn import flash_attn_varlen_qkvpacked_func from flash_attn.bert_padding import pad_input, unpad_input -def flash_attn_no_pad(qkv, - key_padding_mask, - causal=False, - dropout_p=0.0, - softmax_scale=None): +def flash_attn_no_pad(qkv, key_padding_mask, causal=False, dropout_p=0.0, softmax_scale=None): # adapted from https://github.com/Dao-AILab/flash-attention/blob/13403e81157ba37ca525890f2f0f2137edf75311/flash_attn/flash_attention.py#L27 batch_size = qkv.shape[0] seqlen = qkv.shape[1] nheads = qkv.shape[-2] x = rearrange(qkv, "b s three h d -> b s (three h d)") - x_unpad, indices, cu_seqlens, max_s, used_seqlens_in_batch = unpad_input( - x, key_padding_mask) + x_unpad, indices, cu_seqlens, max_s, used_seqlens_in_batch = unpad_input(x, key_padding_mask) - x_unpad = rearrange(x_unpad, - "nnz (three h d) -> nnz three h d", - three=3, - h=nheads) + x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads) output_unpad = flash_attn_varlen_qkvpacked_func( x_unpad, cu_seqlens, @@ -29,8 +21,7 @@ def flash_attn_no_pad(qkv, causal=causal, ) output = rearrange( - pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, - batch_size, seqlen), + pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size, seqlen), "b s (h d) -> b s h d", h=nheads, ) diff --git a/fastvideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py b/fastvideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py index ef9c38b93..23e1c47fc 100644 --- a/fastvideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py +++ b/fastvideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py @@ -32,14 +32,13 @@ from diffusers.models import AutoencoderKL from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.schedulers import KarrasDiffusionSchedulers -from diffusers.utils import (USE_PEFT_BACKEND, BaseOutput, deprecate, logging, - replace_example_docstring, scale_lora_layers) +from diffusers.utils import (USE_PEFT_BACKEND, BaseOutput, deprecate, logging, replace_example_docstring, + scale_lora_layers) from diffusers.utils.torch_utils import randn_tensor from einops import rearrange from fastvideo.utils.communications import all_gather -from fastvideo.utils.parallel_states import (get_sequence_parallel_state, - nccl_info) +from fastvideo.utils.parallel_states import (get_sequence_parallel_state, nccl_info) from ...constants import PRECISION_TO_TYPE from ...modules import HYVideoDiffusionTransformer @@ -56,14 +55,12 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 """ - std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), - keepdim=True) + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) # rescale the results from guidance (fixes overexposure) noise_pred_rescaled = noise_cfg * (std_text / std_cfg) # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images - noise_cfg = (guidance_rescale * noise_pred_rescaled + - (1 - guidance_rescale) * noise_cfg) + noise_cfg = (guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg) return noise_cfg @@ -99,28 +96,22 @@ def retrieve_timesteps( second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: - raise ValueError( - "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" - ) + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") if timesteps is not None: - accepts_timesteps = "timesteps" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys()) + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" timestep schedules. Please check whether you are using the correct scheduler." - ) + f" timestep schedules. Please check whether you are using the correct scheduler.") scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: - accept_sigmas = "sigmas" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys()) + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" sigmas schedules. Please check whether you are using the correct scheduler." - ) + f" sigmas schedules. Please check whether you are using the correct scheduler.") scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) @@ -158,9 +149,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae" _optional_components = ["text_encoder_2"] _exclude_from_cpu_offload = ["transformer"] - _callback_tensor_inputs = [ - "latents", "prompt_embeds", "negative_prompt_embeds" - ] + _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] def __init__( self, @@ -184,8 +173,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): self.args = args # ========================================================================================== - if (hasattr(scheduler.config, "steps_offset") - and scheduler.config.steps_offset != 1): + if (hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1): deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " @@ -193,27 +181,19 @@ class HunyuanVideoPipeline(DiffusionPipeline): " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file") - deprecate("steps_offset!=1", - "1.0.0", - deprecation_message, - standard_warn=False) + deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) - if (hasattr(scheduler.config, "clip_sample") - and scheduler.config.clip_sample is True): + if (hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True): deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" - " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" - ) - deprecate("clip_sample not set", - "1.0.0", - deprecation_message, - standard_warn=False) + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file") + deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) @@ -225,10 +205,8 @@ class HunyuanVideoPipeline(DiffusionPipeline): scheduler=scheduler, text_encoder_2=text_encoder_2, ) - self.vae_scale_factor = 2**(len(self.vae.config.block_out_channels) - - 1) - self.image_processor = VaeImageProcessor( - vae_scale_factor=self.vae_scale_factor) + self.vae_scale_factor = 2**(len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) def encode_prompt( self, @@ -296,14 +274,11 @@ class HunyuanVideoPipeline(DiffusionPipeline): if prompt_embeds is None: # textual inversion: process multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): - prompt = self.maybe_convert_prompt(prompt, - text_encoder.tokenizer) + prompt = self.maybe_convert_prompt(prompt, text_encoder.tokenizer) text_inputs = text_encoder.text2tokens(prompt, data_type=data_type) if clip_skip is None: - prompt_outputs = text_encoder.encode(text_inputs, - data_type=data_type, - device=device) + prompt_outputs = text_encoder.encode(text_inputs, data_type=data_type, device=device) prompt_embeds = prompt_outputs.hidden_state else: prompt_outputs = text_encoder.encode( @@ -315,23 +290,19 @@ class HunyuanVideoPipeline(DiffusionPipeline): # Access the `hidden_states` first, that contains a tuple of # all the hidden states from the encoder layers. Then index into # the tuple to access the hidden states from the desired layer. - prompt_embeds = prompt_outputs.hidden_states_list[-(clip_skip + - 1)] + prompt_embeds = prompt_outputs.hidden_states_list[-(clip_skip + 1)] # We also need to apply the final LayerNorm here to not mess with the # representations. The `last_hidden_states` that we typically use for # obtaining the final prompt representations passes through the LayerNorm # layer. - prompt_embeds = text_encoder.model.text_model.final_layer_norm( - prompt_embeds) + prompt_embeds = text_encoder.model.text_model.final_layer_norm(prompt_embeds) attention_mask = prompt_outputs.attention_mask if attention_mask is not None: attention_mask = attention_mask.to(device) bs_embed, seq_len = attention_mask.shape - attention_mask = attention_mask.repeat(1, - num_videos_per_prompt) - attention_mask = attention_mask.view( - bs_embed * num_videos_per_prompt, seq_len) + attention_mask = attention_mask.repeat(1, num_videos_per_prompt) + attention_mask = attention_mask.view(bs_embed * num_videos_per_prompt, seq_len) if text_encoder is not None: prompt_embeds_dtype = text_encoder.dtype @@ -340,21 +311,18 @@ class HunyuanVideoPipeline(DiffusionPipeline): else: prompt_embeds_dtype = prompt_embeds.dtype - prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, - device=device) + prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) if prompt_embeds.ndim == 2: bs_embed, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt) - prompt_embeds = prompt_embeds.view( - bs_embed * num_videos_per_prompt, -1) + prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, -1) else: bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) - prompt_embeds = prompt_embeds.view( - bs_embed * num_videos_per_prompt, seq_len, -1) + prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, seq_len, -1) return ( prompt_embeds, @@ -365,10 +333,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): def decode_latents(self, latents, enable_tiling=True): deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead" - deprecate("decode_latents", - "1.0.0", - deprecation_message, - standard_warn=False) + deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False) latents = 1 / self.vae.config.scaling_factor * latents if enable_tiling: @@ -409,30 +374,21 @@ class HunyuanVideoPipeline(DiffusionPipeline): vae_ver="88-4c-sd", ): if height % 8 != 0 or width % 8 != 0: - raise ValueError( - f"`height` and `width` have to be divisible by 8 but are {height} and {width}." - ) + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if video_length is not None: if "884" in vae_ver: if video_length != 1 and (video_length - 1) % 4 != 0: - raise ValueError( - f"`video_length` has to be 1 or a multiple of 4 but is {video_length}." - ) + raise ValueError(f"`video_length` has to be 1 or a multiple of 4 but is {video_length}.") elif "888" in vae_ver: if video_length != 1 and (video_length - 1) % 8 != 0: - raise ValueError( - f"`video_length` has to be 1 or a multiple of 8 but is {video_length}." - ) + raise ValueError(f"`video_length` has to be 1 or a multiple of 8 but is {video_length}.") - if callback_steps is not None and (not isinstance(callback_steps, int) - or callback_steps <= 0): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}.") - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs - for k in callback_on_step_end_tensor_inputs): + if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): + raise ValueError(f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}.") + if callback_on_step_end_tensor_inputs is not None and not all(k in self._callback_tensor_inputs + for k in callback_on_step_end_tensor_inputs): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) @@ -443,19 +399,13 @@ class HunyuanVideoPipeline(DiffusionPipeline): " only forward one of the two.") elif prompt is None and prompt_embeds is None: raise ValueError( - "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." - ) - elif prompt is not None and (not isinstance(prompt, str) - and not isinstance(prompt, list)): - raise ValueError( - f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" - ) + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.") + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) + raise ValueError(f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two.") if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: @@ -486,14 +436,10 @@ class HunyuanVideoPipeline(DiffusionPipeline): if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) + f" size of {batch_size}. Make sure the batch size matches the length of the generators.") if latents is None: - latents = randn_tensor(shape, - generator=generator, - device=device, - dtype=dtype) + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) @@ -585,8 +531,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): negative_prompt: Optional[Union[str, List[str]]] = None, num_videos_per_prompt: Optional[int] = 1, eta: float = 0.0, - generator: Optional[Union[torch.Generator, - List[torch.Generator]]] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, @@ -597,8 +542,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, clip_skip: Optional[int] = None, - callback_on_step_end: Optional[Union[Callable[[int, int, Dict], - None], PipelineCallback, + callback_on_step_end: Optional[Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks, ]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], vae_ver: str = "88-4c-sd", @@ -707,8 +651,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) - if isinstance(callback_on_step_end, - (PipelineCallback, MultiPipelineCallbacks)): + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs # 0. Default height and width to unet @@ -744,8 +687,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): else: batch_size = prompt_embeds.shape[0] - device = (torch.device(f"cuda:{dist.get_rank()}") - if dist.is_initialized() else self._execution_device) + device = (torch.device(f"cuda:{dist.get_rank()}") if dist.is_initialized() else self._execution_device) # 3. Encode input prompt lora_scale = (self.cross_attention_kwargs.get("scale", None) @@ -805,15 +747,13 @@ class HunyuanVideoPipeline(DiffusionPipeline): if prompt_mask is not None: prompt_mask = torch.cat([negative_prompt_mask, prompt_mask]) if prompt_embeds_2 is not None: - prompt_embeds_2 = torch.cat( - [negative_prompt_embeds_2, prompt_embeds_2]) + prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2]) if prompt_mask_2 is not None: - prompt_mask_2 = torch.cat( - [negative_prompt_mask_2, prompt_mask_2]) + prompt_mask_2 = torch.cat([negative_prompt_mask_2, prompt_mask_2]) # 4. Prepare timesteps - extra_set_timesteps_kwargs = self.prepare_extra_func_kwargs( - self.scheduler.set_timesteps, {"n_tokens": n_tokens}) + extra_set_timesteps_kwargs = self.prepare_extra_func_kwargs(self.scheduler.set_timesteps, + {"n_tokens": n_tokens}) timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, @@ -844,9 +784,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): ) world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group if get_sequence_parallel_state(): - latents = rearrange(latents, - "b t (n s) h w -> b t n s h w", - n=world_size).contiguous() + latents = rearrange(latents, "b t (n s) h w -> b t n s h w", n=world_size).contiguous() latents = latents[:, :, rank, :, :, :] # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline @@ -859,20 +797,16 @@ class HunyuanVideoPipeline(DiffusionPipeline): ) target_dtype = PRECISION_TO_TYPE[self.args.precision] - autocast_enabled = (target_dtype != - torch.float32) and not self.args.disable_autocast + autocast_enabled = (target_dtype != torch.float32) and not self.args.disable_autocast vae_dtype = PRECISION_TO_TYPE[self.args.vae_precision] - vae_autocast_enabled = ( - vae_dtype != torch.float32) and not self.args.disable_autocast + vae_autocast_enabled = (vae_dtype != torch.float32) and not self.args.disable_autocast # 7. Denoising loop - num_warmup_steps = len( - timesteps) - num_inference_steps * self.scheduler.order + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) def dict_to_3d_list(mask_strategy, t_max=50, l_max=60, h_max=24): - result = [[[None for _ in range(h_max)] for _ in range(l_max)] - for _ in range(t_max)] + result = [[[None for _ in range(h_max)] for _ in range(l_max)] for _ in range(t_max)] if mask_strategy is None: return result for key, value in mask_strategy.items(): @@ -888,33 +822,25 @@ class HunyuanVideoPipeline(DiffusionPipeline): continue # expand the latents if we are doing classifier free guidance - latent_model_input = (torch.cat( - [latents] * - 2) if self.do_classifier_free_guidance else latents) - latent_model_input = self.scheduler.scale_model_input( - latent_model_input, t) + latent_model_input = (torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) t_expand = t.repeat(latent_model_input.shape[0]) guidance_expand = (torch.tensor( [embedded_guidance_scale] * latent_model_input.shape[0], dtype=torch.float32, device=device, - ).to(target_dtype) * 1000.0 if embedded_guidance_scale - is not None else None) + ).to(target_dtype) * 1000.0 if embedded_guidance_scale is not None else None) # predict the noise residual - with torch.autocast(device_type="cuda", - dtype=target_dtype, - enabled=autocast_enabled): + with torch.autocast(device_type="cuda", dtype=target_dtype, enabled=autocast_enabled): # concat prompt_embeds_2 and prompt_embeds. Mismatch fill with zeros if prompt_embeds_2.shape[-1] != prompt_embeds.shape[-1]: prompt_embeds_2 = F.pad( prompt_embeds_2, - (0, prompt_embeds.shape[2] - - prompt_embeds_2.shape[1]), + (0, prompt_embeds.shape[2] - prompt_embeds_2.shape[1]), value=0, ).unsqueeze(1) - encoder_hidden_states = torch.cat( - [prompt_embeds_2, prompt_embeds], dim=1) + encoder_hidden_states = torch.cat([prompt_embeds_2, prompt_embeds], dim=1) noise_pred = self.transformer( # For an input image (129, 192, 336) (1, 256, 256) latent_model_input, encoder_hidden_states, @@ -928,8 +854,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): # perform guidance if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * ( - noise_pred_text - noise_pred_uncond) + noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf @@ -940,29 +865,20 @@ class HunyuanVideoPipeline(DiffusionPipeline): ) # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, - t, - latents, - **extra_step_kwargs, - return_dict=False)[0] + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end( - self, i, t, callback_kwargs) + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop( - "prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop( - "negative_prompt_embeds", negative_prompt_embeds) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) # call the callback, if provided - if i == len(timesteps) - 1 or ( - (i + 1) > num_warmup_steps and - (i + 1) % self.scheduler.order == 0): + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): if progress_bar is not None: progress_bar.update() if callback is not None and i % callback_steps == 0: @@ -982,26 +898,19 @@ class HunyuanVideoPipeline(DiffusionPipeline): pass else: raise ValueError( - f"Only support latents with shape (b, c, h, w) or (b, c, f, h, w), but got {latents.shape}." - ) + f"Only support latents with shape (b, c, h, w) or (b, c, f, h, w), but got {latents.shape}.") - if (hasattr(self.vae.config, "shift_factor") - and self.vae.config.shift_factor): - latents = (latents / self.vae.config.scaling_factor + - self.vae.config.shift_factor) + if (hasattr(self.vae.config, "shift_factor") and self.vae.config.shift_factor): + latents = (latents / self.vae.config.scaling_factor + self.vae.config.shift_factor) else: latents = latents / self.vae.config.scaling_factor - with torch.autocast(device_type="cuda", - dtype=vae_dtype, - enabled=vae_autocast_enabled): + with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled): if enable_tiling: self.vae.enable_tiling() if enable_vae_sp: self.vae.enable_parallel() - image = self.vae.decode(latents, - return_dict=False, - generator=generator)[0] + image = self.vae.decode(latents, return_dict=False, generator=generator)[0] if expand_temporal_dim or image.shape[2] == 1: image = image.squeeze(2) diff --git a/fastvideo/models/hunyuan/diffusion/schedulers/scheduling_flow_match_discrete.py b/fastvideo/models/hunyuan/diffusion/schedulers/scheduling_flow_match_discrete.py index 69ed5317e..bf032ab0e 100644 --- a/fastvideo/models/hunyuan/diffusion/schedulers/scheduling_flow_match_discrete.py +++ b/fastvideo/models/hunyuan/diffusion/schedulers/scheduling_flow_match_discrete.py @@ -80,17 +80,14 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin): self.sigmas = sigmas # the value fed to model - self.timesteps = (sigmas[:-1] * - num_train_timesteps).to(dtype=torch.float32) + self.timesteps = (sigmas[:-1] * num_train_timesteps).to(dtype=torch.float32) self._step_index = None self._begin_index = None self.supported_solver = ["euler"] if solver not in self.supported_solver: - raise ValueError( - f"Solver {solver} not supported. Supported solvers: {self.supported_solver}" - ) + raise ValueError(f"Solver {solver} not supported. Supported solvers: {self.supported_solver}") @property def step_index(self): @@ -146,8 +143,7 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin): sigmas = 1 - sigmas self.sigmas = sigmas - self.timesteps = (sigmas[:-1] * self.config.num_train_timesteps).to( - dtype=torch.float32, device=device) + self.timesteps = (sigmas[:-1] * self.config.num_train_timesteps).to(dtype=torch.float32, device=device) # Reset step index self._step_index = None @@ -174,9 +170,7 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin): else: self._step_index = self._begin_index - def scale_model_input(self, - sample: torch.Tensor, - timestep: Optional[int] = None) -> torch.Tensor: + def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor: return sample def sd3_time_shift(self, t: torch.Tensor): @@ -216,10 +210,9 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin): if (isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) or isinstance(timestep, torch.LongTensor)): - raise ValueError(( - "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" - " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" - " one of the `scheduler.timesteps` as a timestep."), ) + raise ValueError(("Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep."), ) if self.step_index is None: self._init_step_index(timestep) @@ -232,9 +225,7 @@ class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin): if self.config.solver == "euler": prev_sample = sample + model_output.to(torch.float32) * dt else: - raise ValueError( - f"Solver {self.config.solver} not supported. Supported solvers: {self.supported_solver}" - ) + raise ValueError(f"Solver {self.config.solver} not supported. Supported solvers: {self.supported_solver}") # upon completion increase step index by one self._step_index += 1 diff --git a/fastvideo/models/hunyuan/idle_config.py b/fastvideo/models/hunyuan/idle_config.py index f9588d105..7d13c9d0a 100644 --- a/fastvideo/models/hunyuan/idle_config.py +++ b/fastvideo/models/hunyuan/idle_config.py @@ -7,8 +7,7 @@ from .modules.models import HUNYUAN_VIDEO_CONFIG def parse_args(namespace=None): - parser = argparse.ArgumentParser( - description="HunyuanVideo inference script") + parser = argparse.ArgumentParser(description="HunyuanVideo inference script") parser = add_network_args(parser) parser = add_extra_models_args(parser) @@ -36,8 +35,7 @@ def add_network_args(parser: argparse.ArgumentParser): "--latent-channels", type=str, default=16, - help= - "Number of latent channels of DiT. If None, it will be determined by `vae`. If provided, " + help="Number of latent channels of DiT. If None, it will be determined by `vae`. If provided, " "it still needs to match the latent channels of the VAE model.", ) group.add_argument( @@ -45,22 +43,16 @@ def add_network_args(parser: argparse.ArgumentParser): type=str, default="bf16", choices=PRECISIONS, - help= - "Precision mode. Options: fp32, fp16, bf16. Applied to the backbone model and optimizer.", + help="Precision mode. Options: fp32, fp16, bf16. Applied to the backbone model and optimizer.", ) # RoPE - group.add_argument("--rope-theta", - type=int, - default=256, - help="Theta used in RoPE.") + group.add_argument("--rope-theta", type=int, default=256, help="Theta used in RoPE.") return parser def add_extra_models_args(parser: argparse.ArgumentParser): - group = parser.add_argument_group( - title="Extra models args, including vae, text encoders and tokenizers)" - ) + group = parser.add_argument_group(title="Extra models args, including vae, text encoders and tokenizers)") # - VAE group.add_argument( @@ -104,10 +96,7 @@ def add_extra_models_args(parser: argparse.ArgumentParser): default=4096, help="Dimension of the text encoder hidden states.", ) - group.add_argument("--text-len", - type=int, - default=256, - help="Maximum length of the text input.") + group.add_argument("--text-len", type=int, default=256, help="Maximum length of the text input.") group.add_argument( "--tokenizer", type=str, @@ -138,8 +127,7 @@ def add_extra_models_args(parser: argparse.ArgumentParser): group.add_argument( "--apply-final-norm", action="store_true", - help= - "Apply final normalization to the used text encoder hidden states.", + help="Apply final normalization to the used text encoder hidden states.", ) # - CLIP @@ -232,16 +220,13 @@ def add_inference_args(parser: argparse.ArgumentParser): "--model-base", type=str, default="ckpts", - help= - "Root path of all the models, including t2v models and extra models.", + help="Root path of all the models, including t2v models and extra models.", ) group.add_argument( "--dit-weight", type=str, - default= - "ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", - help= - "Path to the HunyuanVideo model. If None, search the model in the args.model_root." + default="ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", + help="Path to the HunyuanVideo model. If None, search the model in the args.model_root." "1. If it is a file, load the model directly." "2. If it is a directory, search the model in the directory. Support two types of models: " "1) named `pytorch_model_*.pt`" @@ -252,15 +237,13 @@ def add_inference_args(parser: argparse.ArgumentParser): type=str, default="540p", choices=["540p", "720p"], - help= - "Root path of all the models, including t2v models and extra models.", + help="Root path of all the models, including t2v models and extra models.", ) group.add_argument( "--load-key", type=str, default="module", - help= - "Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", + help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", ) group.add_argument( "--use-cpu-offload", @@ -284,8 +267,7 @@ def add_inference_args(parser: argparse.ArgumentParser): group.add_argument( "--disable-autocast", action="store_true", - help= - "Disable autocast for denoising loop and vae decoding in pipeline sampling.", + help="Disable autocast for denoising loop and vae decoding in pipeline sampling.", ) group.add_argument( "--save-path", @@ -317,8 +299,7 @@ def add_inference_args(parser: argparse.ArgumentParser): type=int, nargs="+", default=(720, 1280), - help= - "Video size for training. If a single value is provided, it will be used for both height " + help="Video size for training. If a single value is provided, it will be used for both height " "and width. If two values are provided, they will be used for height and width " "respectively.", ) @@ -326,8 +307,7 @@ def add_inference_args(parser: argparse.ArgumentParser): "--video-length", type=int, default=129, - help= - "How many frames to sample from a video. if using 3d vae, the number should be 4n+1", + help="How many frames to sample from a video. if using 3d vae, the number should be 4n+1", ) # --- prompt --- group.add_argument( @@ -341,26 +321,16 @@ def add_inference_args(parser: argparse.ArgumentParser): type=str, default="auto", choices=["file", "random", "fixed", "auto"], - help= - "Seed type for evaluation. If file, use the seed from the CSV file. If random, generate a " + help="Seed type for evaluation. If file, use the seed from the CSV file. If random, generate a " "random seed. If fixed, use the fixed seed given by `--seed`. If auto, `csv` will use the " "seed column if available, otherwise use the fixed `seed` value. `prompt` will use the " "fixed `seed` value.", ) - group.add_argument("--seed", - type=int, - default=None, - help="Seed for evaluation.") + group.add_argument("--seed", type=int, default=None, help="Seed for evaluation.") # Classifier-Free Guidance - group.add_argument("--neg-prompt", - type=str, - default=None, - help="Negative prompt for sampling.") - group.add_argument("--cfg-scale", - type=float, - default=1.0, - help="Classifier free guidance scale.") + group.add_argument("--neg-prompt", type=str, default=None, help="Negative prompt for sampling.") + group.add_argument("--cfg-scale", type=float, default=1.0, help="Classifier free guidance scale.") group.add_argument( "--embedded-cfg-scale", type=float, @@ -371,8 +341,7 @@ def add_inference_args(parser: argparse.ArgumentParser): group.add_argument( "--reproduce", action="store_true", - help= - "Enable reproducibility by setting random seeds and deterministic algorithms.", + help="Enable reproducibility by setting random seeds and deterministic algorithms.", ) return parser @@ -402,14 +371,10 @@ def sanity_check_args(args): # VAE channels vae_pattern = r"\d{2,3}-\d{1,2}c-\w+" if not re.match(vae_pattern, args.vae): - raise ValueError( - f"Invalid VAE model: {args.vae}. Must be in the format of '{vae_pattern}'." - ) + raise ValueError(f"Invalid VAE model: {args.vae}. Must be in the format of '{vae_pattern}'.") vae_channels = int(args.vae.split("-")[1][:-1]) if args.latent_channels is None: args.latent_channels = vae_channels if vae_channels != args.latent_channels: - raise ValueError( - f"Latent channels ({args.latent_channels}) must match the VAE channels ({vae_channels})." - ) + raise ValueError(f"Latent channels ({args.latent_channels}) must match the VAE channels ({vae_channels}).") return args diff --git a/fastvideo/models/hunyuan/inference.py b/fastvideo/models/hunyuan/inference.py index 064c9efdc..124faca31 100644 --- a/fastvideo/models/hunyuan/inference.py +++ b/fastvideo/models/hunyuan/inference.py @@ -7,9 +7,7 @@ import torch from loguru import logger from safetensors.torch import load_file as safetensors_load_file -from fastvideo.models.hunyuan.constants import (NEGATIVE_PROMPT, - PRECISION_TO_TYPE, - PROMPT_TEMPLATE) +from fastvideo.models.hunyuan.constants import (NEGATIVE_PROMPT, PRECISION_TO_TYPE, PROMPT_TEMPLATE) from fastvideo.models.hunyuan.diffusion.pipelines import HunyuanVideoPipeline from fastvideo.models.hunyuan.diffusion.schedulers import \ FlowMatchDiscreteScheduler @@ -47,17 +45,12 @@ class Inference(object): self.use_cpu_offload = use_cpu_offload self.args = args - self.device = (device if device is not None else - "cuda" if torch.cuda.is_available() else "cpu") + self.device = (device if device is not None else "cuda" if torch.cuda.is_available() else "cpu") self.logger = logger self.parallel_args = parallel_args @classmethod - def from_pretrained(cls, - pretrained_model_path, - args, - device=None, - **kwargs): + def from_pretrained(cls, pretrained_model_path, args, device=None, **kwargs): """ Initialize the Inference pipeline. @@ -67,8 +60,7 @@ class Inference(object): device (int): The device for inference. Default is 0. """ # ======================================================================== - logger.info( - f"Got text-to-video model root path: {pretrained_model_path}") + logger.info(f"Got text-to-video model root path: {pretrained_model_path}") # ==================== Initialize Distributed Environment ================ if nccl_info.sp_size > 1: @@ -85,10 +77,7 @@ class Inference(object): # =========================== Build main model =========================== logger.info("Building model...") - factor_kwargs = { - "device": device, - "dtype": PRECISION_TO_TYPE[args.precision] - } + factor_kwargs = {"device": device, "dtype": PRECISION_TO_TYPE[args.precision]} in_channels = args.latent_channels out_channels = args.latent_channels @@ -116,23 +105,19 @@ class Inference(object): # Text encoder if args.prompt_template_video is not None: - crop_start = PROMPT_TEMPLATE[args.prompt_template_video].get( - "crop_start", 0) + crop_start = PROMPT_TEMPLATE[args.prompt_template_video].get("crop_start", 0) elif args.prompt_template is not None: - crop_start = PROMPT_TEMPLATE[args.prompt_template].get( - "crop_start", 0) + crop_start = PROMPT_TEMPLATE[args.prompt_template].get("crop_start", 0) else: crop_start = 0 max_length = args.text_len + crop_start # prompt_template - prompt_template = (PROMPT_TEMPLATE[args.prompt_template] - if args.prompt_template is not None else None) + prompt_template = (PROMPT_TEMPLATE[args.prompt_template] if args.prompt_template is not None else None) # prompt_template_video prompt_template_video = (PROMPT_TEMPLATE[args.prompt_template_video] - if args.prompt_template_video is not None else - None) + if args.prompt_template_video is not None else None) text_encoder = TextEncoder( text_encoder_type=args.text_encoder, @@ -186,23 +171,17 @@ class Inference(object): model_path = dit_weight / f"pytorch_model_{load_key}.pt" bare_model = True elif any(str(f).endswith("_model_states.pt") for f in files): - files = [ - f for f in files if str(f).endswith("_model_states.pt") - ] + files = [f for f in files if str(f).endswith("_model_states.pt")] model_path = files[0] if len(files) > 1: - logger.warning( - f"Multiple model weights found in {dit_weight}, using {model_path}" - ) + logger.warning(f"Multiple model weights found in {dit_weight}, using {model_path}") bare_model = False else: - raise ValueError( - f"Invalid model path: {dit_weight} with unrecognized weight format: " - f"{list(map(str, files))}. When given a directory as --dit-weight, only " - f"`pytorch_model_*.pt`(provided by HunyuanDiT official) and " - f"`*_model_states.pt`(saved by deepspeed) can be parsed. If you want to load a " - f"specific weight file, please provide the full path to the file." - ) + raise ValueError(f"Invalid model path: {dit_weight} with unrecognized weight format: " + f"{list(map(str, files))}. When given a directory as --dit-weight, only " + f"`pytorch_model_*.pt`(provided by HunyuanDiT official) and " + f"`*_model_states.pt`(saved by deepspeed) can be parsed. If you want to load a " + f"specific weight file, please provide the full path to the file.") else: if dit_weight.is_dir(): files = list(dit_weight.glob("*.pt")) @@ -212,23 +191,17 @@ class Inference(object): model_path = dit_weight / f"pytorch_model_{load_key}.pt" bare_model = True elif any(str(f).endswith("_model_states.pt") for f in files): - files = [ - f for f in files if str(f).endswith("_model_states.pt") - ] + files = [f for f in files if str(f).endswith("_model_states.pt")] model_path = files[0] if len(files) > 1: - logger.warning( - f"Multiple model weights found in {dit_weight}, using {model_path}" - ) + logger.warning(f"Multiple model weights found in {dit_weight}, using {model_path}") bare_model = False else: - raise ValueError( - f"Invalid model path: {dit_weight} with unrecognized weight format: " - f"{list(map(str, files))}. When given a directory as --dit-weight, only " - f"`pytorch_model_*.pt`(provided by HunyuanDiT official) and " - f"`*_model_states.pt`(saved by deepspeed) can be parsed. If you want to load a " - f"specific weight file, please provide the full path to the file." - ) + raise ValueError(f"Invalid model path: {dit_weight} with unrecognized weight format: " + f"{list(map(str, files))}. When given a directory as --dit-weight, only " + f"`pytorch_model_*.pt`(provided by HunyuanDiT official) and " + f"`*_model_states.pt`(saved by deepspeed) can be parsed. If you want to load a " + f"specific weight file, please provide the full path to the file.") elif dit_weight.is_file(): model_path = dit_weight bare_model = "unknown" @@ -243,21 +216,18 @@ class Inference(object): state_dict = safetensors_load_file(model_path) elif model_path.suffix == ".pt": # Use torch for .pt files - state_dict = torch.load(model_path, - map_location=lambda storage, loc: storage) + state_dict = torch.load(model_path, map_location=lambda storage, loc: storage) else: raise ValueError(f"Unsupported file format: {model_path}") - if bare_model == "unknown" and ("ema" in state_dict - or "module" in state_dict): + if bare_model == "unknown" and ("ema" in state_dict or "module" in state_dict): bare_model = False if bare_model is False: if load_key in state_dict: state_dict = state_dict[load_key] else: - raise KeyError( - f"Missing key: `{load_key}` in the checkpoint: {model_path}. The keys in the checkpoint " - f"are: {list(state_dict.keys())}.") + raise KeyError(f"Missing key: `{load_key}` in the checkpoint: {model_path}. The keys in the checkpoint " + f"are: {list(state_dict.keys())}.") model.load_state_dict(state_dict, strict=True) return model @@ -266,13 +236,11 @@ class Inference(object): if isinstance(size, int): size = [size] if not isinstance(size, (list, tuple)): - raise ValueError( - f"Size must be an integer or (height, width), got {size}.") + raise ValueError(f"Size must be an integer or (height, width), got {size}.") if len(size) == 1: size = [size[0], size[0]] if len(size) != 2: - raise ValueError( - f"Size must be an integer or (height, width), got {size}.") + raise ValueError(f"Size must be an integer or (height, width), got {size}.") return size @@ -398,36 +366,22 @@ class HunyuanVideoSampler(Inference): if isinstance(seed, torch.Tensor): seed = seed.tolist() if seed is None: - seeds = [ - random.randint(0, 1_000_000) - for _ in range(batch_size * num_videos_per_prompt) - ] + seeds = [random.randint(0, 1_000_000) for _ in range(batch_size * num_videos_per_prompt)] elif isinstance(seed, int): - seeds = [ - seed + i for _ in range(batch_size) - for i in range(num_videos_per_prompt) - ] + seeds = [seed + i for _ in range(batch_size) for i in range(num_videos_per_prompt)] elif isinstance(seed, (list, tuple)): if len(seed) == batch_size: - seeds = [ - int(seed[i]) + j for i in range(batch_size) - for j in range(num_videos_per_prompt) - ] + seeds = [int(seed[i]) + j for i in range(batch_size) for j in range(num_videos_per_prompt)] elif len(seed) == batch_size * num_videos_per_prompt: seeds = [int(s) for s in seed] else: raise ValueError( f"Length of seed must be equal to number of prompt(batch_size) or " - f"batch_size * num_videos_per_prompt ({batch_size} * {num_videos_per_prompt}), got {seed}." - ) + f"batch_size * num_videos_per_prompt ({batch_size} * {num_videos_per_prompt}), got {seed}.") else: - raise ValueError( - f"Seed must be an integer, a list of integers, or None, got {seed}." - ) + raise ValueError(f"Seed must be an integer, a list of integers, or None, got {seed}.") # Peiyuan: using GPU seed will cause A100 and H100 to generate different results... - generator = [ - torch.Generator("cpu").manual_seed(seed) for seed in seeds - ] + generator = [torch.Generator("cpu").manual_seed(seed) for seed in seeds] out_dict["seeds"] = seeds # ======================================================================== @@ -438,13 +392,9 @@ class HunyuanVideoSampler(Inference): f"`height` and `width` and `video_length` must be positive integers, got height={height}, width={width}, video_length={video_length}" ) if (video_length - 1) % 4 != 0: - raise ValueError( - f"`video_length-1` must be a multiple of 4, got {video_length}" - ) + raise ValueError(f"`video_length-1` must be a multiple of 4, got {video_length}") - logger.info( - f"Input (height, width, video_length) = ({height}, {width}, {video_length})" - ) + logger.info(f"Input (height, width, video_length) = ({height}, {width}, {video_length})") target_height = align_to(height, 16) target_width = align_to(width, 16) @@ -456,17 +406,14 @@ class HunyuanVideoSampler(Inference): # Arguments: prompt, new_prompt, negative_prompt # ======================================================================== if not isinstance(prompt, str): - raise TypeError( - f"`prompt` must be a string, but got {type(prompt)}") + raise TypeError(f"`prompt` must be a string, but got {type(prompt)}") prompt = [prompt.strip()] # negative prompt if negative_prompt is None or negative_prompt == "": negative_prompt = self.default_negative_prompt if not isinstance(negative_prompt, str): - raise TypeError( - f"`negative_prompt` must be a string, but got {type(negative_prompt)}" - ) + raise TypeError(f"`negative_prompt` must be a string, but got {type(negative_prompt)}") negative_prompt = [negative_prompt.strip()] # ======================================================================== @@ -480,11 +427,9 @@ class HunyuanVideoSampler(Inference): self.pipeline.scheduler = scheduler if "884" in self.args.vae: - latents_size = [(video_length - 1) // 4 + 1, height // 8, - width // 8] + latents_size = [(video_length - 1) // 4 + 1, height // 8, width // 8] elif "888" in self.args.vae: - latents_size = [(video_length - 1) // 8 + 1, height // 8, - width // 8] + latents_size = [(video_length - 1) // 8 + 1, height // 8, width // 8] n_tokens = latents_size[0] * latents_size[1] * latents_size[2] # ======================================================================== diff --git a/fastvideo/models/hunyuan/modules/attenion.py b/fastvideo/models/hunyuan/modules/attenion.py index 4d64df2fa..4bd20e6f4 100644 --- a/fastvideo/models/hunyuan/modules/attenion.py +++ b/fastvideo/models/hunyuan/modules/attenion.py @@ -5,8 +5,7 @@ from st_attn import sliding_tile_attention from fastvideo.models.flash_attn_no_pad import flash_attn_no_pad from fastvideo.utils.communications import all_gather, all_to_all_4D -from fastvideo.utils.parallel_states import (get_sequence_parallel_state, - nccl_info) +from fastvideo.utils.parallel_states import (get_sequence_parallel_state, nccl_info) def attention( @@ -23,11 +22,7 @@ def attention( if attn_mask is not None and attn_mask.dtype != torch.bool: attn_mask = attn_mask.bool() - x = flash_attn_no_pad(qkv, - attn_mask, - causal=causal, - dropout_p=drop_rate, - softmax_scale=None) + x = flash_attn_no_pad(qkv, attn_mask, causal=causal, dropout_p=drop_rate, softmax_scale=None) b, s, a, d = x.shape out = x.reshape(b, s, -1) @@ -35,48 +30,30 @@ def attention( def tile(x, sp_size): - x = rearrange(x, - "b (sp t h w) head d -> b (t sp h w) head d", - sp=sp_size, - t=30, - h=48 // sp_size, - w=80) - return rearrange( - x, - "b (n_t ts_t n_h ts_h n_w ts_w) h d -> b (n_t n_h n_w ts_t ts_h ts_w) h d", - n_t=5, - n_h=6, - n_w=10, - ts_t=6, - ts_h=8, - ts_w=8) + x = rearrange(x, "b (sp t h w) head d -> b (t sp h w) head d", sp=sp_size, t=30, h=48 // sp_size, w=80) + return rearrange(x, + "b (n_t ts_t n_h ts_h n_w ts_w) h d -> b (n_t n_h n_w ts_t ts_h ts_w) h d", + n_t=5, + n_h=6, + n_w=10, + ts_t=6, + ts_h=8, + ts_w=8) def untile(x, sp_size): - x = rearrange( - x, - "b (n_t n_h n_w ts_t ts_h ts_w) h d -> b (n_t ts_t n_h ts_h n_w ts_w) h d", - n_t=5, - n_h=6, - n_w=10, - ts_t=6, - ts_h=8, - ts_w=8) - return rearrange(x, - "b (t sp h w) head d -> b (sp t h w) head d", - sp=sp_size, - t=30, - h=48 // sp_size, - w=80) + x = rearrange(x, + "b (n_t n_h n_w ts_t ts_h ts_w) h d -> b (n_t ts_t n_h ts_h n_w ts_w) h d", + n_t=5, + n_h=6, + n_w=10, + ts_t=6, + ts_h=8, + ts_w=8) + return rearrange(x, "b (t sp h w) head d -> b (sp t h w) head d", sp=sp_size, t=30, h=48 // sp_size, w=80) -def parallel_attention(q, - k, - v, - img_q_len, - img_kv_len, - text_mask, - mask_strategy=None): +def parallel_attention(q, k, v, img_q_len, img_kv_len, text_mask, mask_strategy=None): query, encoder_query = q key, encoder_key = k value, encoder_value = v @@ -90,8 +67,7 @@ def parallel_attention(q, def shrink_head(encoder_state, dim): local_heads = encoder_state.shape[dim] // nccl_info.sp_size - return encoder_state.narrow( - dim, nccl_info.rank_within_group * local_heads, local_heads) + return encoder_state.narrow(dim, nccl_info.rank_within_group * local_heads, local_heads) encoder_query = shrink_head(encoder_query, dim=2) encoder_key = shrink_head(encoder_key, dim=2) @@ -102,18 +78,14 @@ def parallel_attention(q, encoder_sequence_length = encoder_query.size(1) if mask_strategy[0] is not None: - query = torch.cat([tile(query, nccl_info.sp_size), encoder_query], - dim=1).transpose(1, 2) - key = torch.cat([tile(key, nccl_info.sp_size), encoder_key], - dim=1).transpose(1, 2) - value = torch.cat([tile(value, nccl_info.sp_size), encoder_value], - dim=1).transpose(1, 2) + query = torch.cat([tile(query, nccl_info.sp_size), encoder_query], dim=1).transpose(1, 2) + key = torch.cat([tile(key, nccl_info.sp_size), encoder_key], dim=1).transpose(1, 2) + value = torch.cat([tile(value, nccl_info.sp_size), encoder_value], dim=1).transpose(1, 2) head_num = query.size(1) windows = [mask_strategy[head_idx] for head_idx in range(head_num)] - hidden_states = sliding_tile_attention(query, key, value, windows, - text_length).transpose(1, 2) + hidden_states = sliding_tile_attention(query, key, value, windows, text_length).transpose(1, 2) else: query = torch.cat([query, encoder_query], dim=1) key = torch.cat([key, encoder_key], dim=1) @@ -122,24 +94,17 @@ def parallel_attention(q, qkv = torch.stack([query, key, value], dim=2) attn_mask = F.pad(text_mask, (sequence_length, 0), value=True) - hidden_states = flash_attn_no_pad(qkv, - attn_mask, - causal=False, - dropout_p=0.0, - softmax_scale=None) + hidden_states = flash_attn_no_pad(qkv, attn_mask, causal=False, dropout_p=0.0, softmax_scale=None) - hidden_states, encoder_hidden_states = hidden_states.split_with_sizes( - (sequence_length, encoder_sequence_length), dim=1) + hidden_states, encoder_hidden_states = hidden_states.split_with_sizes((sequence_length, encoder_sequence_length), + dim=1) if mask_strategy[0] is not None: hidden_states = untile(hidden_states, nccl_info.sp_size) if get_sequence_parallel_state(): - hidden_states = all_to_all_4D(hidden_states, - scatter_dim=1, - gather_dim=2) - encoder_hidden_states = all_gather(encoder_hidden_states, - dim=2).contiguous() + hidden_states = all_to_all_4D(hidden_states, scatter_dim=1, gather_dim=2) + encoder_hidden_states = all_gather(encoder_hidden_states, dim=2).contiguous() hidden_states = hidden_states.to(query.dtype) encoder_hidden_states = encoder_hidden_states.to(query.dtype) diff --git a/fastvideo/models/hunyuan/modules/embed_layers.py b/fastvideo/models/hunyuan/modules/embed_layers.py index d2cb9bb5a..38aad89cb 100644 --- a/fastvideo/models/hunyuan/modules/embed_layers.py +++ b/fastvideo/models/hunyuan/modules/embed_layers.py @@ -45,8 +45,7 @@ class PatchEmbed(nn.Module): bias=bias, **factory_kwargs, ) - nn.init.xavier_uniform_( - self.proj.weight.view(self.proj.weight.size(0), -1)) + nn.init.xavier_uniform_(self.proj.weight.view(self.proj.weight.size(0), -1)) if bias: nn.init.zeros_(self.proj.bias) @@ -67,12 +66,7 @@ class TextProjection(nn.Module): Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py """ - def __init__(self, - in_channels, - hidden_size, - act_layer, - dtype=None, - device=None): + def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None): factory_kwargs = {"dtype": dtype, "device": device} super().__init__() self.linear_1 = nn.Linear( @@ -111,14 +105,12 @@ def timestep_embedding(t, dim, max_period=10000): .. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py """ half = dim // 2 - freqs = torch.exp(-math.log(max_period) * - torch.arange(start=0, end=half, dtype=torch.float32) / + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=t.device) args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) return embedding @@ -145,10 +137,7 @@ class TimestepEmbedder(nn.Module): out_size = hidden_size self.mlp = nn.Sequential( - nn.Linear(frequency_embedding_size, - hidden_size, - bias=True, - **factory_kwargs), + nn.Linear(frequency_embedding_size, hidden_size, bias=True, **factory_kwargs), act_layer(), nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs), ) @@ -156,8 +145,6 @@ class TimestepEmbedder(nn.Module): nn.init.normal_(self.mlp[2].weight, std=0.02) def forward(self, t): - t_freq = timestep_embedding(t, self.frequency_embedding_size, - self.max_period).type( - self.mlp[0].weight.dtype) + t_freq = timestep_embedding(t, self.frequency_embedding_size, self.max_period).type(self.mlp[0].weight.dtype) t_emb = self.mlp(t_freq) return t_emb diff --git a/fastvideo/models/hunyuan/modules/mlp_layers.py b/fastvideo/models/hunyuan/modules/mlp_layers.py index f84894eb6..921b85252 100644 --- a/fastvideo/models/hunyuan/modules/mlp_layers.py +++ b/fastvideo/models/hunyuan/modules/mlp_layers.py @@ -32,21 +32,13 @@ class MLP(nn.Module): hidden_channels = hidden_channels or in_channels bias = to_2tuple(bias) drop_probs = to_2tuple(drop) - linear_layer = partial(nn.Conv2d, - kernel_size=1) if use_conv else nn.Linear + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear - self.fc1 = linear_layer(in_channels, - hidden_channels, - bias=bias[0], - **factory_kwargs) + self.fc1 = linear_layer(in_channels, hidden_channels, bias=bias[0], **factory_kwargs) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) - self.norm = (norm_layer(hidden_channels, **factory_kwargs) - if norm_layer is not None else nn.Identity()) - self.fc2 = linear_layer(hidden_channels, - out_features, - bias=bias[1], - **factory_kwargs) + self.norm = (norm_layer(hidden_channels, **factory_kwargs) if norm_layer is not None else nn.Identity()) + self.fc2 = linear_layer(hidden_channels, out_features, bias=bias[1], **factory_kwargs) self.drop2 = nn.Dropout(drop_probs[1]) def forward(self, x): @@ -66,15 +58,9 @@ class MLPEmbedder(nn.Module): def __init__(self, in_dim: int, hidden_dim: int, device=None, dtype=None): factory_kwargs = {"device": device, "dtype": dtype} super().__init__() - self.in_layer = nn.Linear(in_dim, - hidden_dim, - bias=True, - **factory_kwargs) + self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True, **factory_kwargs) self.silu = nn.SiLU() - self.out_layer = nn.Linear(hidden_dim, - hidden_dim, - bias=True, - **factory_kwargs) + self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True, **factory_kwargs) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.out_layer(self.silu(self.in_layer(x))) @@ -83,21 +69,12 @@ class MLPEmbedder(nn.Module): class FinalLayer(nn.Module): """The final layer of DiT.""" - def __init__(self, - hidden_size, - patch_size, - out_channels, - act_layer, - device=None, - dtype=None): + def __init__(self, hidden_size, patch_size, out_channels, act_layer, device=None, dtype=None): factory_kwargs = {"device": device, "dtype": dtype} super().__init__() # Just use LayerNorm for the final layer - self.norm_final = nn.LayerNorm(hidden_size, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) if isinstance(patch_size, int): self.linear = nn.Linear( hidden_size, @@ -117,10 +94,7 @@ class FinalLayer(nn.Module): # Here we don't distinguish between the modulate types. Just use the simple one. self.adaLN_modulation = nn.Sequential( act_layer(), - nn.Linear(hidden_size, - 2 * hidden_size, - bias=True, - **factory_kwargs), + nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs), ) # Zero-initialize the modulation nn.init.zeros_(self.adaLN_modulation[1].weight) diff --git a/fastvideo/models/hunyuan/modules/models.py b/fastvideo/models/hunyuan/modules/models.py index b4e741e2d..c46c717e9 100644 --- a/fastvideo/models/hunyuan/modules/models.py +++ b/fastvideo/models/hunyuan/modules/models.py @@ -53,31 +53,17 @@ class MMDoubleStreamBlock(nn.Module): act_layer=get_activation_layer("silu"), **factory_kwargs, ) - self.img_norm1 = nn.LayerNorm(hidden_size, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) + self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) - self.img_attn_qkv = nn.Linear(hidden_size, - hidden_size * 3, - bias=qkv_bias, - **factory_kwargs) + self.img_attn_qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs) qk_norm_layer = get_norm_layer(qk_norm_type) - self.img_attn_q_norm = (qk_norm_layer( - head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.img_attn_q_norm = (qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()) - self.img_attn_k_norm = (qk_norm_layer( - head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.img_attn_k_norm = (qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()) - self.img_attn_proj = nn.Linear(hidden_size, - hidden_size, - bias=qkv_bias, - **factory_kwargs) + self.img_attn_proj = nn.Linear(hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs) - self.img_norm2 = nn.LayerNorm(hidden_size, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) + self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.img_mlp = MLP( hidden_size, mlp_hidden_dim, @@ -92,30 +78,16 @@ class MMDoubleStreamBlock(nn.Module): act_layer=get_activation_layer("silu"), **factory_kwargs, ) - self.txt_norm1 = nn.LayerNorm(hidden_size, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) + self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) - self.txt_attn_qkv = nn.Linear(hidden_size, - hidden_size * 3, - bias=qkv_bias, - **factory_kwargs) - self.txt_attn_q_norm = (qk_norm_layer( - head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.txt_attn_qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs) + self.txt_attn_q_norm = (qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()) - self.txt_attn_k_norm = (qk_norm_layer( - head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.txt_attn_k_norm = (qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()) - self.txt_attn_proj = nn.Linear(hidden_size, - hidden_size, - bias=qkv_bias, - **factory_kwargs) + self.txt_attn_proj = nn.Linear(hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs) - self.txt_norm2 = nn.LayerNorm(hidden_size, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) + self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.txt_mlp = MLP( hidden_size, mlp_hidden_dim, @@ -159,14 +131,9 @@ class MMDoubleStreamBlock(nn.Module): # Prepare image for attention. img_modulated = self.img_norm1(img) - img_modulated = modulate(img_modulated, - shift=img_mod1_shift, - scale=img_mod1_scale) + img_modulated = modulate(img_modulated, shift=img_mod1_shift, scale=img_mod1_scale) img_qkv = self.img_attn_qkv(img_modulated) - img_q, img_k, img_v = rearrange(img_qkv, - "B L (K H D) -> K B L H D", - K=3, - H=self.heads_num) + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) # Apply QK-Norm if needed img_q = self.img_attn_q_norm(img_q).to(img_v) img_k = self.img_attn_k_norm(img_k).to(img_v) @@ -176,34 +143,23 @@ class MMDoubleStreamBlock(nn.Module): def shrink_head(encoder_state, dim): local_heads = encoder_state.shape[dim] // nccl_info.sp_size - return encoder_state.narrow( - dim, nccl_info.rank_within_group * local_heads, - local_heads) + return encoder_state.narrow(dim, nccl_info.rank_within_group * local_heads, local_heads) freqs_cis = ( shrink_head(freqs_cis[0], dim=0), shrink_head(freqs_cis[1], dim=0), ) - img_qq, img_kk = apply_rotary_emb(img_q, - img_k, - freqs_cis, - head_first=False) - assert ( - img_qq.shape == img_q.shape and img_kk.shape == img_k.shape - ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}" + img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False) + assert (img_qq.shape == img_q.shape and img_kk.shape == img_k.shape + ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}" img_q, img_k = img_qq, img_kk # Prepare txt for attention. txt_modulated = self.txt_norm1(txt) - txt_modulated = modulate(txt_modulated, - shift=txt_mod1_shift, - scale=txt_mod1_scale) + txt_modulated = modulate(txt_modulated, shift=txt_mod1_shift, scale=txt_mod1_scale) txt_qkv = self.txt_attn_qkv(txt_modulated) - txt_q, txt_k, txt_v = rearrange(txt_qkv, - "B L (K H D) -> K B L H D", - K=3, - H=self.heads_num) + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) # Apply QK-Norm if needed. txt_q = self.txt_attn_q_norm(txt_q).to(txt_v) txt_k = self.txt_attn_k_norm(txt_k).to(txt_v) @@ -223,24 +179,16 @@ class MMDoubleStreamBlock(nn.Module): img_attn, txt_attn = attn[:, :img.shape[1]], attn[:, img.shape[1]:] # Calculate the img blocks. - img = img + apply_gate(self.img_attn_proj(img_attn), - gate=img_mod1_gate) + img = img + apply_gate(self.img_attn_proj(img_attn), gate=img_mod1_gate) img = img + apply_gate( - self.img_mlp( - modulate(self.img_norm2(img), - shift=img_mod2_shift, - scale=img_mod2_scale)), + self.img_mlp(modulate(self.img_norm2(img), shift=img_mod2_shift, scale=img_mod2_scale)), gate=img_mod2_gate, ) # Calculate the txt blocks. - txt = txt + apply_gate(self.txt_attn_proj(txt_attn), - gate=txt_mod1_gate) + txt = txt + apply_gate(self.txt_attn_proj(txt_attn), gate=txt_mod1_gate) txt = txt + apply_gate( - self.txt_mlp( - modulate(self.txt_norm2(txt), - shift=txt_mod2_shift, - scale=txt_mod2_scale)), + self.txt_mlp(modulate(self.txt_norm2(txt), shift=txt_mod2_shift, scale=txt_mod2_scale)), gate=txt_mod2_gate, ) return img, txt @@ -278,24 +226,17 @@ class MMSingleStreamBlock(nn.Module): self.scale = qk_scale or head_dim**-0.5 # qkv and mlp_in - self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + mlp_hidden_dim, - **factory_kwargs) + self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + mlp_hidden_dim, **factory_kwargs) # proj and mlp_out - self.linear2 = nn.Linear(hidden_size + mlp_hidden_dim, hidden_size, - **factory_kwargs) + self.linear2 = nn.Linear(hidden_size + mlp_hidden_dim, hidden_size, **factory_kwargs) qk_norm_layer = get_norm_layer(qk_norm_type) - self.q_norm = (qk_norm_layer( - head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.q_norm = (qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()) - self.k_norm = (qk_norm_layer( - head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.k_norm = (qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()) - self.pre_norm = nn.LayerNorm(hidden_size, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) + self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.mlp_act = get_activation_layer(mlp_act_type)() self.modulation = ModulateDiT( @@ -323,14 +264,9 @@ class MMSingleStreamBlock(nn.Module): ) -> torch.Tensor: mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1) x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale) - qkv, mlp = torch.split(self.linear1(x_mod), - [3 * self.hidden_size, self.mlp_hidden_dim], - dim=-1) + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) - q, k, v = rearrange(qkv, - "B L (K H D) -> K B L H D", - K=3, - H=self.heads_num) + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) # Apply QK-Norm if needed. q = self.q_norm(q).to(v) @@ -338,8 +274,7 @@ class MMSingleStreamBlock(nn.Module): def shrink_head(encoder_state, dim): local_heads = encoder_state.shape[dim] // nccl_info.sp_size - return encoder_state.narrow( - dim, nccl_info.rank_within_group * local_heads, local_heads) + return encoder_state.narrow(dim, nccl_info.rank_within_group * local_heads, local_heads) freqs_cis = ( shrink_head(freqs_cis[0], dim=0), @@ -349,13 +284,9 @@ class MMSingleStreamBlock(nn.Module): img_q, txt_q = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :] img_k, txt_k = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :] img_v, txt_v = v[:, :-txt_len, :, :], v[:, -txt_len:, :, :] - img_qq, img_kk = apply_rotary_emb(img_q, - img_k, - freqs_cis, - head_first=False) - assert ( - img_qq.shape == img_q.shape and img_kk.shape == img_k.shape - ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}" + img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False) + assert (img_qq.shape == img_q.shape and img_kk.shape == img_k.shape + ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}" img_q, img_k = img_qq, img_kk attn = parallel_attention( @@ -468,19 +399,15 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): self.text_projection = text_projection if hidden_size % heads_num != 0: - raise ValueError( - f"Hidden size {hidden_size} must be divisible by heads_num {heads_num}" - ) + raise ValueError(f"Hidden size {hidden_size} must be divisible by heads_num {heads_num}") pe_dim = hidden_size // heads_num if sum(rope_dim_list) != pe_dim: - raise ValueError( - f"Got {rope_dim_list} but expected positional dim {pe_dim}") + raise ValueError(f"Got {rope_dim_list} but expected positional dim {pe_dim}") self.hidden_size = hidden_size self.heads_num = heads_num # image projection - self.img_in = PatchEmbed(self.patch_size, self.in_channels, - self.hidden_size, **factory_kwargs) + self.img_in = PatchEmbed(self.patch_size, self.in_channels, self.hidden_size, **factory_kwargs) # text projection if self.text_projection == "linear": @@ -499,21 +426,16 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): **factory_kwargs, ) else: - raise NotImplementedError( - f"Unsupported text_projection: {self.text_projection}") + raise NotImplementedError(f"Unsupported text_projection: {self.text_projection}") # time modulation - self.time_in = TimestepEmbedder(self.hidden_size, - get_activation_layer("silu"), - **factory_kwargs) + self.time_in = TimestepEmbedder(self.hidden_size, get_activation_layer("silu"), **factory_kwargs) # text modulation - self.vector_in = MLPEmbedder(self.config.text_states_dim_2, - self.hidden_size, **factory_kwargs) + self.vector_in = MLPEmbedder(self.config.text_states_dim_2, self.hidden_size, **factory_kwargs) # guidance modulation - self.guidance_in = (TimestepEmbedder( - self.hidden_size, get_activation_layer("silu"), **factory_kwargs) + self.guidance_in = (TimestepEmbedder(self.hidden_size, get_activation_layer("silu"), **factory_kwargs) if guidance_embed else None) # double blocks @@ -569,12 +491,8 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): head_dim = self.hidden_size // self.heads_num rope_dim_list = self.rope_dim_list if rope_dim_list is None: - rope_dim_list = [ - head_dim // target_ndim for _ in range(target_ndim) - ] - assert ( - sum(rope_dim_list) == head_dim - ), "sum(rope_dim_list) should equal to head_dim of attention layer" + rope_dim_list = [head_dim // target_ndim for _ in range(target_ndim)] + assert (sum(rope_dim_list) == head_dim), "sum(rope_dim_list) should equal to head_dim of attention layer" freqs_cos, freqs_sin = get_nd_rotary_pos_embed( rope_dim_list, rope_sizes, @@ -605,15 +523,12 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): guidance=None, ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: if guidance is None: - guidance = torch.tensor([6016.0], - device=hidden_states.device, - dtype=torch.bfloat16) + guidance = torch.tensor([6016.0], device=hidden_states.device, dtype=torch.bfloat16) img = x = hidden_states text_mask = encoder_attention_mask t = timestep txt = encoder_hidden_states[:, 1:] - text_states_2 = encoder_hidden_states[:, 0, :self.config. - text_states_dim_2] + text_states_2 = encoder_hidden_states[:, 0, :self.config.text_states_dim_2] _, _, ot, oh, ow = x.shape # codespell:ignore tt, th, tw = ( ot // self.patch_size[0], # codespell:ignore @@ -631,9 +546,7 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): # guidance modulation if self.guidance_embed: if guidance is None: - raise ValueError( - "Didn't get guidance strength for guidance distilled model." - ) + raise ValueError("Didn't get guidance strength for guidance distilled model.") # our timestep_embedding is merged into guidance_in(TimestepEmbedder) vec = vec + self.guidance_in(guidance) @@ -643,11 +556,9 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): if self.text_projection == "linear": txt = self.txt_in(txt) elif self.text_projection == "single_refiner": - txt = self.txt_in(txt, t, - text_mask if self.use_attention_mask else None) + txt = self.txt_in(txt, t, text_mask if self.use_attention_mask else None) else: - raise NotImplementedError( - f"Unsupported text_projection: {self.text_projection}") + raise NotImplementedError(f"Unsupported text_projection: {self.text_projection}") txt_seq_len = txt.shape[1] img_seq_len = img.shape[1] @@ -656,9 +567,7 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): # --------------------- Pass through DiT blocks ------------------------ for index, block in enumerate(self.double_blocks): - double_block_args = [ - img, txt, vec, freqs_cis, text_mask, mask_strategy[index] - ] + double_block_args = [img, txt, vec, freqs_cis, text_mask, mask_strategy[index]] img, txt = block(*double_block_args) # Merge txt and img to pass through single stream blocks. x = torch.cat((img, txt), 1) @@ -681,8 +590,7 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): img = x[:, :img_seq_len, ...] # ---------------------------- Final layer ------------------------------ - img = self.final_layer(img, - vec) # (N, T, patch_size ** 2 * out_channels) + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) img = self.unpatchify(img, tt, th, tw) assert not return_dict, "return_dict is not supported." @@ -711,18 +619,18 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): counts = { "double": sum([ - sum(p.numel() for p in block.img_attn_qkv.parameters()) + - sum(p.numel() for p in block.img_attn_proj.parameters()) + - sum(p.numel() for p in block.img_mlp.parameters()) + - sum(p.numel() for p in block.txt_attn_qkv.parameters()) + - sum(p.numel() for p in block.txt_attn_proj.parameters()) + - sum(p.numel() for p in block.txt_mlp.parameters()) + sum(p.numel() + for p in block.img_attn_qkv.parameters()) + sum(p.numel() + for p in block.img_attn_proj.parameters()) + + sum(p.numel() for p in block.img_mlp.parameters()) + sum(p.numel() + for p in block.txt_attn_qkv.parameters()) + + sum(p.numel() for p in block.txt_attn_proj.parameters()) + sum(p.numel() + for p in block.txt_mlp.parameters()) for block in self.double_blocks ]), "single": sum([ - sum(p.numel() for p in block.linear1.parameters()) + - sum(p.numel() for p in block.linear2.parameters()) + sum(p.numel() for p in block.linear1.parameters()) + sum(p.numel() for p in block.linear2.parameters()) for block in self.single_blocks ]), "total": diff --git a/fastvideo/models/hunyuan/modules/modulate_layers.py b/fastvideo/models/hunyuan/modules/modulate_layers.py index c7a363692..c02ef3187 100644 --- a/fastvideo/models/hunyuan/modules/modulate_layers.py +++ b/fastvideo/models/hunyuan/modules/modulate_layers.py @@ -18,10 +18,7 @@ class ModulateDiT(nn.Module): factory_kwargs = {"dtype": dtype, "device": device} super().__init__() self.act = act_layer() - self.linear = nn.Linear(hidden_size, - factor * hidden_size, - bias=True, - **factory_kwargs) + self.linear = nn.Linear(hidden_size, factor * hidden_size, bias=True, **factory_kwargs) # Zero-initialize the modulation nn.init.zeros_(self.linear.weight) nn.init.zeros_(self.linear.bias) @@ -152,5 +149,4 @@ def get_norm_layer(norm_layer): elif norm_layer == "rms": return RMSNorm else: - raise NotImplementedError( - f"Norm layer {norm_layer} is not implemented") + raise NotImplementedError(f"Norm layer {norm_layer} is not implemented") diff --git a/fastvideo/models/hunyuan/modules/norm_layers.py b/fastvideo/models/hunyuan/modules/norm_layers.py index 0e1e590e7..5bc997059 100644 --- a/fastvideo/models/hunyuan/modules/norm_layers.py +++ b/fastvideo/models/hunyuan/modules/norm_layers.py @@ -75,5 +75,4 @@ def get_norm_layer(norm_layer): elif norm_layer == "rms": return RMSNorm else: - raise NotImplementedError( - f"Norm layer {norm_layer} is not implemented") + raise NotImplementedError(f"Norm layer {norm_layer} is not implemented") diff --git a/fastvideo/models/hunyuan/modules/posemb_layers.py b/fastvideo/models/hunyuan/modules/posemb_layers.py index 2c92471e4..34dcdb5d5 100644 --- a/fastvideo/models/hunyuan/modules/posemb_layers.py +++ b/fastvideo/models/hunyuan/modules/posemb_layers.py @@ -100,19 +100,13 @@ def reshape_for_broadcast( x.shape[-2], x.shape[-1], ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}" - shape = [ - d if i == ndim - 2 or i == ndim - 1 else 1 - for i, d in enumerate(x.shape) - ] + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] else: assert freqs_cis[0].shape == ( x.shape[1], x.shape[-1], ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}" - shape = [ - d if i == 1 or i == ndim - 1 else 1 - for i, d in enumerate(x.shape) - ] + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) else: # freqs_cis: values in complex space @@ -121,25 +115,18 @@ def reshape_for_broadcast( x.shape[-2], x.shape[-1], ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}" - shape = [ - d if i == ndim - 2 or i == ndim - 1 else 1 - for i, d in enumerate(x.shape) - ] + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] else: assert freqs_cis.shape == ( x.shape[1], x.shape[-1], ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}" - shape = [ - d if i == 1 or i == ndim - 1 else 1 - for i, d in enumerate(x.shape) - ] + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis.view(*shape) def rotate_half(x): - x_real, x_imag = (x.float().reshape(*x.shape[:-1], -1, - 2).unbind(-1)) # [B, S, H, D//2] + x_real, x_imag = (x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)) # [B, S, H, D//2] return torch.stack([-x_imag, x_real], dim=-1).flatten(3) @@ -177,15 +164,12 @@ def apply_rotary_emb( xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk) else: # view_as_complex will pack [..., D/2, 2](real) to [..., D/2](complex) - xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, - 2)) # [B, S, H, D//2] - freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to( - xq.device) # [S, D//2] --> [1, S, 1, D//2] + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2] + freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2] # (real, imag) * (cos, sin) = (real * cos - imag * sin, imag * cos + real * sin) # view_as_real will expand [..., D/2](complex) to [..., D/2, 2](real) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) - xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, - 2)) # [B, S, H, D//2] + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2] xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) return xq_out, xk_out @@ -219,28 +203,21 @@ def get_nd_rotary_pos_embed( pos_embed (torch.Tensor): [HW, D/2] """ - grid = get_meshgrid_nd(start, *args, - dim=len(rope_dim_list)) # [3, W, H, D] / [2, W, H] + grid = get_meshgrid_nd(start, *args, dim=len(rope_dim_list)) # [3, W, H, D] / [2, W, H] - if isinstance(theta_rescale_factor, int) or isinstance( - theta_rescale_factor, float): + if isinstance(theta_rescale_factor, int) or isinstance(theta_rescale_factor, float): theta_rescale_factor = [theta_rescale_factor] * len(rope_dim_list) - elif isinstance(theta_rescale_factor, - list) and len(theta_rescale_factor) == 1: + elif isinstance(theta_rescale_factor, list) and len(theta_rescale_factor) == 1: theta_rescale_factor = [theta_rescale_factor[0]] * len(rope_dim_list) assert len(theta_rescale_factor) == len( - rope_dim_list - ), "len(theta_rescale_factor) should equal to len(rope_dim_list)" + rope_dim_list), "len(theta_rescale_factor) should equal to len(rope_dim_list)" - if isinstance(interpolation_factor, int) or isinstance( - interpolation_factor, float): + if isinstance(interpolation_factor, int) or isinstance(interpolation_factor, float): interpolation_factor = [interpolation_factor] * len(rope_dim_list) - elif isinstance(interpolation_factor, - list) and len(interpolation_factor) == 1: + elif isinstance(interpolation_factor, list) and len(interpolation_factor) == 1: interpolation_factor = [interpolation_factor[0]] * len(rope_dim_list) assert len(interpolation_factor) == len( - rope_dim_list - ), "len(interpolation_factor) should equal to len(rope_dim_list)" + rope_dim_list), "len(interpolation_factor) should equal to len(rope_dim_list)" # use 1/ndim of dimensions to encode grid_axis embs = [] @@ -300,8 +277,7 @@ def get_1d_rotary_pos_embed( if theta_rescale_factor != 1.0: theta *= theta_rescale_factor**(dim / (dim - 2)) - freqs = 1.0 / (theta**(torch.arange(0, dim, 2)[:(dim // 2)].float() / dim) - ) # [D/2] + freqs = 1.0 / (theta**(torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) # [D/2] # assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}" freqs = torch.outer(pos * interpolation_factor, freqs) # [S, D/2] if use_real: @@ -309,6 +285,5 @@ def get_1d_rotary_pos_embed( freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] return freqs_cos, freqs_sin else: - freqs_cis = torch.polar(torch.ones_like(freqs), - freqs) # complex64 # [S, D/2] + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] return freqs_cis diff --git a/fastvideo/models/hunyuan/modules/token_refiner.py b/fastvideo/models/hunyuan/modules/token_refiner.py index ea6cb9c4d..6aafc1dba 100644 --- a/fastvideo/models/hunyuan/modules/token_refiner.py +++ b/fastvideo/models/hunyuan/modules/token_refiner.py @@ -33,30 +33,16 @@ class IndividualTokenRefinerBlock(nn.Module): head_dim = hidden_size // heads_num mlp_hidden_dim = int(hidden_size * mlp_width_ratio) - self.norm1 = nn.LayerNorm(hidden_size, - elementwise_affine=True, - eps=1e-6, - **factory_kwargs) - self.self_attn_qkv = nn.Linear(hidden_size, - hidden_size * 3, - bias=qkv_bias, - **factory_kwargs) + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.self_attn_qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs) qk_norm_layer = get_norm_layer(qk_norm_type) - self.self_attn_q_norm = (qk_norm_layer( - head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.self_attn_q_norm = (qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()) - self.self_attn_k_norm = (qk_norm_layer( - head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + self.self_attn_k_norm = (qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()) - self.self_attn_proj = nn.Linear(hidden_size, - hidden_size, - bias=qkv_bias, - **factory_kwargs) + self.self_attn_proj = nn.Linear(hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs) - self.norm2 = nn.LayerNorm(hidden_size, - elementwise_affine=True, - eps=1e-6, - **factory_kwargs) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs) act_layer = get_activation_layer(act_type) self.mlp = MLP( in_channels=hidden_size, @@ -68,10 +54,7 @@ class IndividualTokenRefinerBlock(nn.Module): self.adaLN_modulation = nn.Sequential( act_layer(), - nn.Linear(hidden_size, - 2 * hidden_size, - bias=True, - **factory_kwargs), + nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs), ) # Zero-initialize the modulation nn.init.zeros_(self.adaLN_modulation[1].weight) @@ -80,18 +63,14 @@ class IndividualTokenRefinerBlock(nn.Module): def forward( self, x: torch.Tensor, - c: torch. - Tensor, # timestep_aware_representations + context_aware_representations + c: torch.Tensor, # timestep_aware_representations + context_aware_representations attn_mask: torch.Tensor = None, ): gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1) norm_x = self.norm1(x) qkv = self.self_attn_qkv(norm_x) - q, k, v = rearrange(qkv, - "B L (K H D) -> K B L H D", - K=3, - H=self.heads_num) + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) # Apply QK-Norm if needed q = self.self_attn_q_norm(q).to(v) k = self.self_attn_k_norm(k).to(v) @@ -179,18 +158,13 @@ class SingleTokenRefiner(nn.Module): self.attn_mode = attn_mode assert self.attn_mode == "torch", "Only support 'torch' mode for token refiner." - self.input_embedder = nn.Linear(in_channels, - hidden_size, - bias=True, - **factory_kwargs) + self.input_embedder = nn.Linear(in_channels, hidden_size, bias=True, **factory_kwargs) act_layer = get_activation_layer(act_type) # Build timestep embedding layer - self.t_embedder = TimestepEmbedder(hidden_size, act_layer, - **factory_kwargs) + self.t_embedder = TimestepEmbedder(hidden_size, act_layer, **factory_kwargs) # Build context embedding layer - self.c_embedder = TextProjection(in_channels, hidden_size, act_layer, - **factory_kwargs) + self.c_embedder = TextProjection(in_channels, hidden_size, act_layer, **factory_kwargs) self.individual_token_refiner = IndividualTokenRefiner( hidden_size=hidden_size, @@ -217,10 +191,8 @@ class SingleTokenRefiner(nn.Module): context_aware_representations = x.mean(dim=1) else: mask_float = mask.float().unsqueeze(-1) # [b, s1, 1] - context_aware_representations = (x * mask_float).sum( - dim=1) / mask_float.sum(dim=1) - context_aware_representations = self.c_embedder( - context_aware_representations) + context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1) + context_aware_representations = self.c_embedder(context_aware_representations) c = timestep_aware_representations + context_aware_representations x = self.input_embedder(x) diff --git a/fastvideo/models/hunyuan/text_encoder/__init__.py b/fastvideo/models/hunyuan/text_encoder/__init__.py index 18cd88e18..aadfaa87f 100644 --- a/fastvideo/models/hunyuan/text_encoder/__init__.py +++ b/fastvideo/models/hunyuan/text_encoder/__init__.py @@ -23,24 +23,20 @@ def load_text_encoder( if text_encoder_path is None: text_encoder_path = TEXT_ENCODER_PATH[text_encoder_type] if logger is not None: - logger.info( - f"Loading text encoder model ({text_encoder_type}) from: {text_encoder_path}" - ) + logger.info(f"Loading text encoder model ({text_encoder_type}) from: {text_encoder_path}") if text_encoder_type == "clipL": text_encoder = CLIPTextModel.from_pretrained(text_encoder_path) text_encoder.final_layer_norm = text_encoder.text_model.final_layer_norm elif text_encoder_type == "llm": - text_encoder = AutoModel.from_pretrained(text_encoder_path, - low_cpu_mem_usage=True) + text_encoder = AutoModel.from_pretrained(text_encoder_path, low_cpu_mem_usage=True) text_encoder.final_layer_norm = text_encoder.norm else: raise ValueError(f"Unsupported text encoder type: {text_encoder_type}") # from_pretrained will ensure that the model is in eval mode. if text_encoder_precision is not None: - text_encoder = text_encoder.to( - dtype=PRECISION_TO_TYPE[text_encoder_precision]) + text_encoder = text_encoder.to(dtype=PRECISION_TO_TYPE[text_encoder_precision]) text_encoder.requires_grad_(False) @@ -53,22 +49,16 @@ def load_text_encoder( return text_encoder, text_encoder_path -def load_tokenizer(tokenizer_type, - tokenizer_path=None, - padding_side="right", - logger=None): +def load_tokenizer(tokenizer_type, tokenizer_path=None, padding_side="right", logger=None): if tokenizer_path is None: tokenizer_path = TOKENIZER_PATH[tokenizer_type] if logger is not None: - logger.info( - f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}") + logger.info(f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}") if tokenizer_type == "clipL": - tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, - max_length=77) + tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, max_length=77) elif tokenizer_type == "llm": - tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, - padding_side=padding_side) + tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, padding_side=padding_side) else: raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}") @@ -125,16 +115,12 @@ class TextEncoder(nn.Module): self.max_length = max_length self.precision = text_encoder_precision self.model_path = text_encoder_path - self.tokenizer_type = (tokenizer_type if tokenizer_type is not None - else text_encoder_type) - self.tokenizer_path = (tokenizer_path if tokenizer_path is not None - else text_encoder_path) + self.tokenizer_type = (tokenizer_type if tokenizer_type is not None else text_encoder_type) + self.tokenizer_path = (tokenizer_path if tokenizer_path is not None else text_encoder_path) self.use_attention_mask = use_attention_mask if prompt_template_video is not None: - assert (use_attention_mask is True - ), "Attention mask is True required when training videos." - self.input_max_length = (input_max_length if input_max_length - is not None else max_length) + assert (use_attention_mask is True), "Attention mask is True required when training videos." + self.input_max_length = (input_max_length if input_max_length is not None else max_length) self.prompt_template = prompt_template self.prompt_template_video = prompt_template_video self.hidden_state_skip_layer = hidden_state_skip_layer @@ -144,10 +130,8 @@ class TextEncoder(nn.Module): self.use_template = self.prompt_template is not None if self.use_template: - assert ( - isinstance(self.prompt_template, dict) - and "template" in self.prompt_template - ), f"`prompt_template` must be a dictionary with a key 'template', got {self.prompt_template}" + assert (isinstance(self.prompt_template, dict) and "template" in self.prompt_template + ), f"`prompt_template` must be a dictionary with a key 'template', got {self.prompt_template}" assert "{}" in str(self.prompt_template["template"]), ( "`prompt_template['template']` must contain a placeholder `{}` for the input text, " f"got {self.prompt_template['template']}") @@ -156,8 +140,7 @@ class TextEncoder(nn.Module): if self.use_video_template: if self.prompt_template_video is not None: assert ( - isinstance(self.prompt_template_video, dict) - and "template" in self.prompt_template_video + isinstance(self.prompt_template_video, dict) and "template" in self.prompt_template_video ), f"`prompt_template_video` must be a dictionary with a key 'template', got {self.prompt_template_video}" assert "{}" in str(self.prompt_template_video["template"]), ( "`prompt_template_video['template']` must contain a placeholder `{}` for the input text, " @@ -170,8 +153,7 @@ class TextEncoder(nn.Module): elif "llm" in text_encoder_type or "glm" in text_encoder_type: self.output_key = output_key or "last_hidden_state" else: - raise ValueError( - f"Unsupported text encoder type: {text_encoder_type}") + raise ValueError(f"Unsupported text encoder type: {text_encoder_type}") self.model, self.model_path = load_text_encoder( text_encoder_type=self.text_encoder_type, @@ -226,10 +208,7 @@ class TextEncoder(nn.Module): else: raise ValueError(f"Unsupported data type: {data_type}") if isinstance(text, (list, tuple)): - text = [ - self.apply_text_to_template(one_text, prompt_template) - for one_text in text - ] + text = [self.apply_text_to_template(one_text, prompt_template) for one_text in text] if isinstance(text[0], list): tokenize_input_type = "list" elif isinstance(text, str): @@ -262,8 +241,7 @@ class TextEncoder(nn.Module): **kwargs, ) else: - raise ValueError( - f"Unsupported tokenize_input_type: {tokenize_input_type}") + raise ValueError(f"Unsupported tokenize_input_type: {tokenize_input_type}") def encode( self, @@ -291,27 +269,21 @@ class TextEncoder(nn.Module): return_texts (bool): Whether to return the decoded texts. Defaults to False. """ device = self.model.device if device is None else device - use_attention_mask = use_default(use_attention_mask, - self.use_attention_mask) - hidden_state_skip_layer = use_default(hidden_state_skip_layer, - self.hidden_state_skip_layer) + use_attention_mask = use_default(use_attention_mask, self.use_attention_mask) + hidden_state_skip_layer = use_default(hidden_state_skip_layer, self.hidden_state_skip_layer) do_sample = use_default(do_sample, not self.reproduce) - attention_mask = (batch_encoding["attention_mask"].to(device) - if use_attention_mask else None) + attention_mask = (batch_encoding["attention_mask"].to(device) if use_attention_mask else None) outputs = self.model( input_ids=batch_encoding["input_ids"].to(device), attention_mask=attention_mask, - output_hidden_states=output_hidden_states - or hidden_state_skip_layer is not None, + output_hidden_states=output_hidden_states or hidden_state_skip_layer is not None, ) if hidden_state_skip_layer is not None: - last_hidden_state = outputs.hidden_states[-( - hidden_state_skip_layer + 1)] + last_hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)] # Real last hidden state already has layer norm applied. So here we only apply it # for intermediate layers. if hidden_state_skip_layer > 0 and self.apply_final_norm: - last_hidden_state = self.model.final_layer_norm( - last_hidden_state) + last_hidden_state = self.model.final_layer_norm(last_hidden_state) else: last_hidden_state = outputs[self.output_key] @@ -325,12 +297,10 @@ class TextEncoder(nn.Module): raise ValueError(f"Unsupported data type: {data_type}") if crop_start > 0: last_hidden_state = last_hidden_state[:, crop_start:] - attention_mask = (attention_mask[:, crop_start:] - if use_attention_mask else None) + attention_mask = (attention_mask[:, crop_start:] if use_attention_mask else None) if output_hidden_states: - return TextEncoderModelOutput(last_hidden_state, attention_mask, - outputs.hidden_states) + return TextEncoderModelOutput(last_hidden_state, attention_mask, outputs.hidden_states) return TextEncoderModelOutput(last_hidden_state, attention_mask) def forward( diff --git a/fastvideo/models/hunyuan/utils/file_utils.py b/fastvideo/models/hunyuan/utils/file_utils.py index c87a95eff..82e244f03 100644 --- a/fastvideo/models/hunyuan/utils/file_utils.py +++ b/fastvideo/models/hunyuan/utils/file_utils.py @@ -45,11 +45,7 @@ def safe_file(path): return path -def save_videos_grid(videos: torch.Tensor, - path: str, - rescale=False, - n_rows=1, - fps=24): +def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=1, fps=24): """save videos by video tensor copy from https://github.com/guoyww/AnimateDiff/blob/e92bd5671ba62c0d774a32951453e328018b7c5b/animatediff/utils/util.py#L61 diff --git a/fastvideo/models/hunyuan/vae/__init__.py b/fastvideo/models/hunyuan/vae/__init__.py index f570423e6..761c7cc1f 100644 --- a/fastvideo/models/hunyuan/vae/__init__.py +++ b/fastvideo/models/hunyuan/vae/__init__.py @@ -31,8 +31,7 @@ def load_vae( logger.info(f"Loading 3D VAE model ({vae_type}) from: {vae_path}") config = AutoencoderKLCausal3D.load_config(vae_path) if sample_size: - vae = AutoencoderKLCausal3D.from_config(config, - sample_size=sample_size) + vae = AutoencoderKLCausal3D.from_config(config, sample_size=sample_size) else: vae = AutoencoderKLCausal3D.from_config(config) @@ -43,10 +42,7 @@ def load_vae( if "state_dict" in ckpt: ckpt = ckpt["state_dict"] if any(k.startswith("vae.") for k in ckpt.keys()): - ckpt = { - k.replace("vae.", ""): v - for k, v in ckpt.items() if k.startswith("vae.") - } + ckpt = {k.replace("vae.", ""): v for k, v in ckpt.items() if k.startswith("vae.")} vae.load_state_dict(ckpt) spatial_compression_ratio = vae.config.spatial_compression_ratio diff --git a/fastvideo/models/hunyuan/vae/autoencoder_kl_causal_3d.py b/fastvideo/models/hunyuan/vae/autoencoder_kl_causal_3d.py index 1461fb164..83ef640e5 100644 --- a/fastvideo/models/hunyuan/vae/autoencoder_kl_causal_3d.py +++ b/fastvideo/models/hunyuan/vae/autoencoder_kl_causal_3d.py @@ -35,15 +35,13 @@ except ImportError: from diffusers.loaders.single_file_model import ( FromOriginalModelMixin as FromOriginalVAEMixin, ) -from diffusers.models.attention_processor import ( - ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, Attention, - AttentionProcessor, AttnAddedKVProcessor, AttnProcessor) +from diffusers.models.attention_processor import (ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, Attention, + AttentionProcessor, AttnAddedKVProcessor, AttnProcessor) from diffusers.models.modeling_outputs import AutoencoderKLOutput from diffusers.models.modeling_utils import ModelMixin from diffusers.utils.accelerate_utils import apply_forward_hook -from .vae import (BaseOutput, DecoderCausal3D, DecoderOutput, - DiagonalGaussianDistribution, EncoderCausal3D) +from .vae import (BaseOutput, DecoderCausal3D, DecoderOutput, DiagonalGaussianDistribution, EncoderCausal3D) @dataclass @@ -113,12 +111,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): mid_block_add_attention=mid_block_add_attention, ) - self.quant_conv = nn.Conv3d(2 * latent_channels, - 2 * latent_channels, - kernel_size=1) - self.post_quant_conv = nn.Conv3d(latent_channels, - latent_channels, - kernel_size=1) + self.quant_conv = nn.Conv3d(2 * latent_channels, 2 * latent_channels, kernel_size=1) + self.post_quant_conv = nn.Conv3d(latent_channels, latent_channels, kernel_size=1) self.use_slicing = False self.use_spatial_tiling = False @@ -130,11 +124,9 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): self.tile_latent_min_tsize = sample_tsize // time_compression_ratio self.tile_sample_min_size = self.config.sample_size - sample_size = (self.config.sample_size[0] if isinstance( - self.config.sample_size, - (list, tuple)) else self.config.sample_size) - self.tile_latent_min_size = int( - sample_size / (2**(len(self.config.block_out_channels) - 1))) + sample_size = (self.config.sample_size[0] if isinstance(self.config.sample_size, + (list, tuple)) else self.config.sample_size) + self.tile_latent_min_size = int(sample_size / (2**(len(self.config.block_out_channels) - 1))) self.tile_overlap_factor = 0.25 def _set_gradient_checkpointing(self, module, value=False): @@ -207,12 +199,10 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): processors: Dict[str, AttentionProcessor], ): if hasattr(module, "get_processor"): - processors[f"{name}.processor"] = module.get_processor( - return_deprecated_lora=True) + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, - processors) + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors @@ -244,21 +234,17 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) + f" number of attention layers: {count}. Please make sure to pass {count} processor classes.") - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, - processor): + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor, _remove_lora=_remove_lora) else: - module.set_processor(processor.pop(f"{name}.processor"), - _remove_lora=_remove_lora) + module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora) for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, - processor) + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) @@ -268,11 +254,9 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): """ Disables custom attention processors and sets the default attention implementation. """ - if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS - for proc in self.attn_processors.values()): + if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnAddedKVProcessor() - elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS - for proc in self.attn_processors.values()): + elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnProcessor() else: raise ValueError( @@ -282,11 +266,9 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): self.set_attn_processor(processor, _remove_lora=True) @apply_forward_hook - def encode( - self, - x: torch.FloatTensor, - return_dict: bool = True - ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]: + def encode(self, + x: torch.FloatTensor, + return_dict: bool = True) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]: """ Encode a batch of images/videos into latents. @@ -304,9 +286,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): if self.use_temporal_tiling and x.shape[2] > self.tile_sample_min_tsize: return self.temporal_tiled_encode(x, return_dict=return_dict) - if self.use_spatial_tiling and ( - x.shape[-1] > self.tile_sample_min_size - or x.shape[-2] > self.tile_sample_min_size): + if self.use_spatial_tiling and (x.shape[-1] > self.tile_sample_min_size + or x.shape[-2] > self.tile_sample_min_size): return self.spatial_tiled_encode(x, return_dict=return_dict) if self.use_slicing and x.shape[0] > 1: @@ -323,11 +304,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): return AutoencoderKLOutput(latent_dist=posterior) - def _decode( - self, - z: torch.FloatTensor, - return_dict: bool = True - ) -> Union[DecoderOutput, torch.FloatTensor]: + def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: assert len(z.shape) == 5, "The input tensor should have 5 dimensions." if self.use_parallel: @@ -336,9 +313,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): if self.use_temporal_tiling and z.shape[2] > self.tile_latent_min_tsize: return self.temporal_tiled_decode(z, return_dict=return_dict) - if self.use_spatial_tiling and ( - z.shape[-1] > self.tile_latent_min_size - or z.shape[-2] > self.tile_latent_min_size): + if self.use_spatial_tiling and (z.shape[-1] > self.tile_latent_min_size + or z.shape[-2] > self.tile_latent_min_size): return self.spatial_tiled_decode(z, return_dict=return_dict) z = self.post_quant_conv(z) @@ -369,9 +345,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): """ if self.use_slicing and z.shape[0] > 1: - decoded_slices = [ - self._decode(z_slice).sample for z_slice in z.split(1) - ] + decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)] decoded = torch.cat(decoded_slices) else: decoded = self._decode(z).sample @@ -381,28 +355,26 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): return DecoderOutput(sample=decoded) - def blend_v(self, a: torch.Tensor, b: torch.Tensor, - blend_extent: int) -> torch.Tensor: + def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: blend_extent = min(a.shape[-2], b.shape[-2], blend_extent) for y in range(blend_extent): - b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * ( - 1 - y / blend_extent) + b[:, :, :, y, :] * (y / blend_extent) + b[:, :, :, + y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (y / blend_extent) return b - def blend_h(self, a: torch.Tensor, b: torch.Tensor, - blend_extent: int) -> torch.Tensor: + def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: blend_extent = min(a.shape[-1], b.shape[-1], blend_extent) for x in range(blend_extent): - b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * ( - 1 - x / blend_extent) + b[:, :, :, :, x] * (x / blend_extent) + b[:, :, :, :, + x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (x / blend_extent) return b - def blend_t(self, a: torch.Tensor, b: torch.Tensor, - blend_extent: int) -> torch.Tensor: + def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor: blend_extent = min(a.shape[-3], b.shape[-3], blend_extent) for x in range(blend_extent): - b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * ( - 1 - x / blend_extent) + b[:, :, x, :, :] * (x / blend_extent) + b[:, :, + x, :, :] = a[:, :, -blend_extent + x, :, :] * (1 - x / blend_extent) + b[:, :, + x, :, :] * (x / blend_extent) return b def spatial_tiled_encode( @@ -429,10 +401,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned. """ - overlap_size = int(self.tile_sample_min_size * - (1 - self.tile_overlap_factor)) - blend_extent = int(self.tile_latent_min_size * - self.tile_overlap_factor) + overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor) row_limit = self.tile_latent_min_size - blend_extent # Split video into tiles and encode them separately. @@ -440,8 +410,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): for i in range(0, x.shape[-2], overlap_size): row = [] for j in range(0, x.shape[-1], overlap_size): - tile = x[:, :, :, i:i + self.tile_sample_min_size, - j:j + self.tile_sample_min_size, ] + tile = x[:, :, :, i:i + self.tile_sample_min_size, j:j + self.tile_sample_min_size, ] tile = self.encoder(tile) tile = self.quant_conv(tile) row.append(tile) @@ -471,8 +440,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): def spatial_tiled_decode(self, z: torch.FloatTensor, - return_dict: bool = True - ) -> Union[DecoderOutput, torch.FloatTensor]: + return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: r""" Decode a batch of images/videos using a tiled decoder. @@ -486,10 +454,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is returned. """ - overlap_size = int(self.tile_latent_min_size * - (1 - self.tile_overlap_factor)) - blend_extent = int(self.tile_sample_min_size * - self.tile_overlap_factor) + overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor) row_limit = self.tile_sample_min_size - blend_extent # Split z into overlapping tiles and decode them separately. @@ -498,8 +464,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): for i in range(0, z.shape[-2], overlap_size): row = [] for j in range(0, z.shape[-1], overlap_size): - tile = z[:, :, :, i:i + self.tile_latent_min_size, - j:j + self.tile_latent_min_size, ] + tile = z[:, :, :, i:i + self.tile_latent_min_size, j:j + self.tile_latent_min_size, ] tile = self.post_quant_conv(tile) decoded = self.decoder(tile) row.append(decoded) @@ -523,24 +488,19 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): return DecoderOutput(sample=dec) - def temporal_tiled_encode(self, - x: torch.FloatTensor, - return_dict: bool = True) -> AutoencoderKLOutput: + def temporal_tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput: B, C, T, H, W = x.shape - overlap_size = int(self.tile_sample_min_tsize * - (1 - self.tile_overlap_factor)) - blend_extent = int(self.tile_latent_min_tsize * - self.tile_overlap_factor) + overlap_size = int(self.tile_sample_min_tsize * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_latent_min_tsize * self.tile_overlap_factor) t_limit = self.tile_latent_min_tsize - blend_extent # Split the video into tiles and encode them separately. row = [] for i in range(0, T, overlap_size): tile = x[:, :, i:i + self.tile_sample_min_tsize + 1, :, :] - if self.use_spatial_tiling and ( - tile.shape[-1] > self.tile_sample_min_size - or tile.shape[-2] > self.tile_sample_min_size): + if self.use_spatial_tiling and (tile.shape[-1] > self.tile_sample_min_size + or tile.shape[-2] > self.tile_sample_min_size): tile = self.spatial_tiled_encode(tile, return_moments=True) else: tile = self.encoder(tile) @@ -566,25 +526,20 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): def temporal_tiled_decode(self, z: torch.FloatTensor, - return_dict: bool = True - ) -> Union[DecoderOutput, torch.FloatTensor]: + return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: # Split z into overlapping tiles and decode them separately. B, C, T, H, W = z.shape - overlap_size = int(self.tile_latent_min_tsize * - (1 - self.tile_overlap_factor)) - blend_extent = int(self.tile_sample_min_tsize * - self.tile_overlap_factor) + overlap_size = int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_sample_min_tsize * self.tile_overlap_factor) t_limit = self.tile_sample_min_tsize - blend_extent row = [] for i in range(0, T, overlap_size): tile = z[:, :, i:i + self.tile_latent_min_tsize + 1, :, :] - if self.use_spatial_tiling and ( - tile.shape[-1] > self.tile_latent_min_size - or tile.shape[-2] > self.tile_latent_min_size): - decoded = self.spatial_tiled_decode(tile, - return_dict=True).sample + if self.use_spatial_tiling and (tile.shape[-1] > self.tile_latent_min_size + or tile.shape[-2] > self.tile_latent_min_size): + decoded = self.spatial_tiled_decode(tile, return_dict=True).sample else: tile = self.post_quant_conv(tile) decoded = self.decoder(tile) @@ -605,22 +560,19 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): return DecoderOutput(sample=dec) - def _parallel_data_generator(self, gathered_results, - gathered_dim_metadata): + def _parallel_data_generator(self, gathered_results, gathered_dim_metadata): global_idx = 0 for i, per_rank_metadata in enumerate(gathered_dim_metadata): _start_shape = 0 for shape in per_rank_metadata: mul_shape = prod(shape) - yield (gathered_results[i, _start_shape:_start_shape + - mul_shape].reshape(shape), global_idx) + yield (gathered_results[i, _start_shape:_start_shape + mul_shape].reshape(shape), global_idx) _start_shape += mul_shape global_idx += 1 def parallel_tiled_decode(self, z: torch.FloatTensor, - return_dict: bool = True - ) -> Union[DecoderOutput, torch.FloatTensor]: + return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: """ Parallel version of tiled_decode that distributes both temporal and spatial computation across GPUs """ @@ -628,16 +580,12 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): B, C, T, H, W = z.shape # Calculate parameters - t_overlap_size = int(self.tile_latent_min_tsize * - (1 - self.tile_overlap_factor)) - t_blend_extent = int(self.tile_sample_min_tsize * - self.tile_overlap_factor) + t_overlap_size = int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor)) + t_blend_extent = int(self.tile_sample_min_tsize * self.tile_overlap_factor) t_limit = self.tile_sample_min_tsize - t_blend_extent - s_overlap_size = int(self.tile_latent_min_size * - (1 - self.tile_overlap_factor)) - s_blend_extent = int(self.tile_sample_min_size * - self.tile_overlap_factor) + s_overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)) + s_blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor) s_row_limit = self.tile_sample_min_size - s_blend_extent # Calculate tile dimensions @@ -655,8 +603,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): local_results = [] local_dim_metadata = [] # Process assigned tiles - for local_idx, global_idx in enumerate( - range(start_tile_idx, end_tile_idx)): + for local_idx, global_idx in enumerate(range(start_tile_idx, end_tile_idx)): # Convert flat index to 3D indices t_idx = global_idx // total_spatial_tiles spatial_idx = global_idx % total_spatial_tiles @@ -670,8 +617,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): # Extract and process tile tile = z[:, :, t_start:t_start + self.tile_latent_min_tsize + 1, - h_start:h_start + self.tile_latent_min_size, - w_start:w_start + self.tile_latent_min_size] + h_start:h_start + self.tile_latent_min_size, w_start:w_start + self.tile_latent_min_size] # Process tile tile = self.post_quant_conv(tile) @@ -691,13 +637,8 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): del local_results torch.cuda.empty_cache() # first gather size to pad the results - local_size = torch.tensor([results.size(0)], - device=results.device, - dtype=torch.int64) - all_sizes = [ - torch.zeros(1, device=results.device, dtype=torch.int64) - for _ in range(world_size) - ] + local_size = torch.tensor([results.size(0)], device=results.device, dtype=torch.int64) + all_sizes = [torch.zeros(1, device=results.device, dtype=torch.int64) for _ in range(world_size)] dist.all_gather(all_sizes, local_size) max_size = max(size.item() for size in all_sizes) padded_results = torch.zeros(max_size, device=results.device) @@ -707,16 +648,13 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): # Gather all results gathered_dim_metadata = [None] * world_size gathered_results = torch.zeros_like(padded_results).repeat( - world_size, *[1] * len(padded_results.shape) - ).contiguous( - ) # use contiguous to make sure it won't copy data in the following operations + world_size, *[1] * len(padded_results.shape)).contiguous( + ) # use contiguous to make sure it won't copy data in the following operations dist.all_gather_into_tensor(gathered_results, padded_results) dist.all_gather_object(gathered_dim_metadata, local_dim_metadata) # Process gathered results - data = [[[[] for _ in range(num_w_tiles)] for _ in range(num_h_tiles)] - for _ in range(num_t_tiles)] - for current_data, global_idx in self._parallel_data_generator( - gathered_results, gathered_dim_metadata): + data = [[[[] for _ in range(num_w_tiles)] for _ in range(num_h_tiles)] for _ in range(num_t_tiles)] + for current_data, global_idx in self._parallel_data_generator(gathered_results, gathered_dim_metadata): t_idx = global_idx // total_spatial_tiles spatial_idx = global_idx % total_spatial_tiles h_idx = spatial_idx // num_w_tiles @@ -726,11 +664,9 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): result_slices = [] last_slice_data = None for i, tem_data in enumerate(data): - slice_data = self._merge_spatial_tiles(tem_data, s_blend_extent, - s_row_limit) + slice_data = self._merge_spatial_tiles(tem_data, s_blend_extent, s_row_limit) if i > 0: - slice_data = self.blend_t(last_slice_data, slice_data, - t_blend_extent) + slice_data = self.blend_t(last_slice_data, slice_data, t_blend_extent) result_slices.append(slice_data[:, :, :t_limit, :, :]) else: result_slices.append(slice_data[:, :, :t_limit + 1, :, :]) @@ -748,8 +684,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): result_row = [] for j, tile in enumerate(row): if i > 0: - tile = self.blend_v(spatial_rows[i - 1][j], tile, - blend_extent) + tile = self.blend_v(spatial_rows[i - 1][j], tile, blend_extent) if j > 0: tile = self.blend_h(row[j - 1], tile, blend_extent) result_row.append(tile[:, :, :, :row_limit, :row_limit]) @@ -806,9 +741,7 @@ class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): for _, attn_processor in self.attn_processors.items(): if "Added" in str(attn_processor.__class__.__name__): - raise ValueError( - "`fuse_qkv_projections()` is not supported for models having added KV projections." - ) + raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") self.original_attn_processors = self.attn_processors diff --git a/fastvideo/models/hunyuan/vae/unet_causal_3d_blocks.py b/fastvideo/models/hunyuan/vae/unet_causal_3d_blocks.py index 37dce3993..3c466f55f 100644 --- a/fastvideo/models/hunyuan/vae/unet_causal_3d_blocks.py +++ b/fastvideo/models/hunyuan/vae/unet_causal_3d_blocks.py @@ -31,16 +31,9 @@ from torch import nn logger = logging.get_logger(__name__) # pylint: disable=invalid-name -def prepare_causal_attention_mask(n_frame: int, - n_hw: int, - dtype, - device, - batch_size: int = None): +def prepare_causal_attention_mask(n_frame: int, n_hw: int, dtype, device, batch_size: int = None): seq_len = n_frame * n_hw - mask = torch.full((seq_len, seq_len), - float("-inf"), - dtype=dtype, - device=device) + mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device) for i in range(seq_len): i_frame = i // n_hw mask[i, :(i_frame + 1) * n_hw] = 0 @@ -78,12 +71,7 @@ class CausalConv3d(nn.Module): ) # W, H, T self.time_causal_padding = padding - self.conv = nn.Conv3d(chan_in, - chan_out, - kernel_size, - stride=stride, - dilation=dilation, - **kwargs) + self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs) def forward(self, x): x = F.pad(x, self.time_causal_padding, mode=self.pad_mode) @@ -135,10 +123,7 @@ class UpsampleCausal3D(nn.Module): elif use_conv: if kernel_size is None: kernel_size = 3 - conv = CausalConv3d(self.channels, - self.out_channels, - kernel_size=kernel_size, - bias=bias) + conv = CausalConv3d(self.channels, self.out_channels, kernel_size=kernel_size, bias=bias) if name == "conv": self.conv = conv @@ -175,14 +160,10 @@ class UpsampleCausal3D(nn.Module): first_h, other_h = hidden_states.split((1, T - 1), dim=2) if output_size is None: if T > 1: - other_h = F.interpolate(other_h, - scale_factor=self.upsample_factor, - mode="nearest") + other_h = F.interpolate(other_h, scale_factor=self.upsample_factor, mode="nearest") first_h = first_h.squeeze(2) - first_h = F.interpolate(first_h, - scale_factor=self.upsample_factor[1:], - mode="nearest") + first_h = F.interpolate(first_h, scale_factor=self.upsample_factor[1:], mode="nearest") first_h = first_h.unsqueeze(2) else: raise NotImplementedError @@ -260,15 +241,11 @@ class DownsampleCausal3D(nn.Module): else: self.conv = conv - def forward(self, - hidden_states: torch.FloatTensor, - scale: float = 1.0) -> torch.FloatTensor: + def forward(self, hidden_states: torch.FloatTensor, scale: float = 1.0) -> torch.FloatTensor: assert hidden_states.shape[1] == self.channels if self.norm is not None: - hidden_states = self.norm(hidden_states.permute(0, 2, 3, - 1)).permute( - 0, 3, 1, 2) + hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) assert hidden_states.shape[1] == self.channels @@ -325,58 +302,36 @@ class ResnetBlockCausal3D(nn.Module): groups_out = groups if self.time_embedding_norm == "ada_group": - self.norm1 = AdaGroupNorm(temb_channels, - in_channels, - groups, - eps=eps) + self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps) elif self.time_embedding_norm == "spatial": self.norm1 = SpatialNorm(in_channels, temb_channels) else: - self.norm1 = torch.nn.GroupNorm(num_groups=groups, - num_channels=in_channels, - eps=eps, - affine=True) + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) - self.conv1 = CausalConv3d(in_channels, - out_channels, - kernel_size=3, - stride=1) + self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1) if temb_channels is not None: if self.time_embedding_norm == "default": self.time_emb_proj = linear_cls(temb_channels, out_channels) elif self.time_embedding_norm == "scale_shift": - self.time_emb_proj = linear_cls(temb_channels, - 2 * out_channels) - elif (self.time_embedding_norm == "ada_group" - or self.time_embedding_norm == "spatial"): + self.time_emb_proj = linear_cls(temb_channels, 2 * out_channels) + elif (self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial"): self.time_emb_proj = None else: - raise ValueError( - f"Unknown time_embedding_norm : {self.time_embedding_norm} " - ) + raise ValueError(f"Unknown time_embedding_norm : {self.time_embedding_norm} ") else: self.time_emb_proj = None if self.time_embedding_norm == "ada_group": - self.norm2 = AdaGroupNorm(temb_channels, - out_channels, - groups_out, - eps=eps) + self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps) elif self.time_embedding_norm == "spatial": self.norm2 = SpatialNorm(out_channels, temb_channels) else: - self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, - num_channels=out_channels, - eps=eps, - affine=True) + self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) self.dropout = torch.nn.Dropout(dropout) conv_3d_out_channels = conv_3d_out_channels or out_channels - self.conv2 = CausalConv3d(out_channels, - conv_3d_out_channels, - kernel_size=3, - stride=1) + self.conv2 = CausalConv3d(out_channels, conv_3d_out_channels, kernel_size=3, stride=1) self.nonlinearity = get_activation(non_linearity) @@ -384,12 +339,10 @@ class ResnetBlockCausal3D(nn.Module): if self.up: self.upsample = UpsampleCausal3D(in_channels, use_conv=False) elif self.down: - self.downsample = DownsampleCausal3D(in_channels, - use_conv=False, - name="op") + self.downsample = DownsampleCausal3D(in_channels, use_conv=False, name="op") - self.use_in_shortcut = (self.in_channels != conv_3d_out_channels if - use_in_shortcut is None else use_in_shortcut) + self.use_in_shortcut = (self.in_channels != conv_3d_out_channels + if use_in_shortcut is None else use_in_shortcut) self.conv_shortcut = None if self.use_in_shortcut: @@ -409,8 +362,7 @@ class ResnetBlockCausal3D(nn.Module): ) -> torch.FloatTensor: hidden_states = input_tensor - if (self.time_embedding_norm == "ada_group" - or self.time_embedding_norm == "spatial"): + if (self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial"): hidden_states = self.norm1(hidden_states, temb) else: hidden_states = self.norm1(hidden_states) @@ -438,8 +390,7 @@ class ResnetBlockCausal3D(nn.Module): if temb is not None and self.time_embedding_norm == "default": hidden_states = hidden_states + temb - if (self.time_embedding_norm == "ada_group" - or self.time_embedding_norm == "spatial"): + if (self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial"): hidden_states = self.norm2(hidden_states, temb) else: hidden_states = self.norm2(hidden_states) @@ -456,8 +407,7 @@ class ResnetBlockCausal3D(nn.Module): if self.conv_shortcut is not None: input_tensor = self.conv_shortcut(input_tensor) - output_tensor = (input_tensor + - hidden_states) / self.output_scale_factor + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor return output_tensor @@ -497,9 +447,7 @@ def get_down_block3d( ) attention_head_dim = num_attention_heads - down_block_type = (down_block_type[7:] - if down_block_type.startswith("UNetRes") else - down_block_type) + down_block_type = (down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type) if down_block_type == "DownEncoderBlockCausal3D": return DownEncoderBlockCausal3D( num_layers=num_layers, @@ -553,8 +501,7 @@ def get_up_block3d( ) attention_head_dim = num_attention_heads - up_block_type = (up_block_type[7:] - if up_block_type.startswith("UNetRes") else up_block_type) + up_block_type = (up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type) if up_block_type == "UpDecoderBlockCausal3D": return UpDecoderBlockCausal3D( num_layers=num_layers, @@ -595,13 +542,11 @@ class UNetMidBlockCausal3D(nn.Module): output_scale_factor: float = 1.0, ): super().__init__() - resnet_groups = (resnet_groups if resnet_groups is not None else min( - in_channels // 4, 32)) + resnet_groups = (resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)) self.add_attention = add_attention if attn_groups is None: - attn_groups = (resnet_groups - if resnet_time_scale_shift == "default" else None) + attn_groups = (resnet_groups if resnet_time_scale_shift == "default" else None) # there is always at least one resnet resnets = [ @@ -636,9 +581,7 @@ class UNetMidBlockCausal3D(nn.Module): rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=attn_groups, - spatial_norm_dim=(temb_channels - if resnet_time_scale_shift - == "spatial" else None), + spatial_norm_dim=(temb_channels if resnet_time_scale_shift == "spatial" else None), residual_connection=True, bias=True, upcast_softmax=True, @@ -664,29 +607,19 @@ class UNetMidBlockCausal3D(nn.Module): self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) - def forward(self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: + def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): if attn is not None: B, C, T, H, W = hidden_states.shape - hidden_states = rearrange(hidden_states, - "b c f h w -> b (f h w) c") - attention_mask = prepare_causal_attention_mask( - T, - H * W, - hidden_states.dtype, - hidden_states.device, - batch_size=B) - hidden_states = attn(hidden_states, - temb=temb, - attention_mask=attention_mask) - hidden_states = rearrange(hidden_states, - "b (f h w) c -> b c f h w", - f=T, - h=H, - w=W) + hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c") + attention_mask = prepare_causal_attention_mask(T, + H * W, + hidden_states.dtype, + hidden_states.device, + batch_size=B) + hidden_states = attn(hidden_states, temb=temb, attention_mask=attention_mask) + hidden_states = rearrange(hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W) hidden_states = resnet(hidden_states, temb) return hidden_states @@ -745,9 +678,7 @@ class DownEncoderBlockCausal3D(nn.Module): else: self.downsamplers = None - def forward(self, - hidden_states: torch.FloatTensor, - scale: float = 1.0) -> torch.FloatTensor: + def forward(self, hidden_states: torch.FloatTensor, scale: float = 1.0) -> torch.FloatTensor: for resnet in self.resnets: hidden_states = resnet(hidden_states, temb=None, scale=scale) @@ -761,21 +692,21 @@ class DownEncoderBlockCausal3D(nn.Module): class UpDecoderBlockCausal3D(nn.Module): def __init__( - self, - in_channels: int, - out_channels: int, - resolution_idx: Optional[int] = None, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", # default, spatial - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor: float = 1.0, - add_upsample: bool = True, - upsample_scale_factor=(2, 2, 2), - temb_channels: Optional[int] = None, + self, + in_channels: int, + out_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", # default, spatial + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + upsample_scale_factor=(2, 2, 2), + temb_channels: Optional[int] = None, ): super().__init__() resnets = [] diff --git a/fastvideo/models/hunyuan/vae/vae.py b/fastvideo/models/hunyuan/vae/vae.py index 117da9afe..5ba12c68b 100644 --- a/fastvideo/models/hunyuan/vae/vae.py +++ b/fastvideo/models/hunyuan/vae/vae.py @@ -8,8 +8,7 @@ from diffusers.models.attention_processor import SpatialNorm from diffusers.utils import BaseOutput, is_torch_version from diffusers.utils.torch_utils import randn_tensor -from .unet_causal_3d_blocks import (CausalConv3d, UNetMidBlockCausal3D, - get_down_block3d, get_up_block3d) +from .unet_causal_3d_blocks import (CausalConv3d, UNetMidBlockCausal3D, get_down_block3d, get_up_block3d) @dataclass @@ -47,10 +46,7 @@ class EncoderCausal3D(nn.Module): super().__init__() self.layers_per_block = layers_per_block - self.conv_in = CausalConv3d(in_channels, - block_out_channels[0], - kernel_size=3, - stride=1) + self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1) self.mid_block = None self.down_blocks = nn.ModuleList([]) @@ -60,33 +56,25 @@ class EncoderCausal3D(nn.Module): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 - num_spatial_downsample_layers = int( - np.log2(spatial_compression_ratio)) + num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio)) num_time_downsample_layers = int(np.log2(time_compression_ratio)) if time_compression_ratio == 4: - add_spatial_downsample = bool( - i < num_spatial_downsample_layers) - add_time_downsample = bool( - i >= - (len(block_out_channels) - 1 - num_time_downsample_layers) - and not is_final_block) + add_spatial_downsample = bool(i < num_spatial_downsample_layers) + add_time_downsample = bool(i >= (len(block_out_channels) - 1 - num_time_downsample_layers) + and not is_final_block) else: - raise ValueError( - f"Unsupported time_compression_ratio: {time_compression_ratio}." - ) + raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}.") downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1) downsample_stride_T = (2, ) if add_time_downsample else (1, ) - downsample_stride = tuple(downsample_stride_T + - downsample_stride_HW) + downsample_stride = tuple(downsample_stride_T + downsample_stride_HW) down_block = get_down_block3d( down_block_type, num_layers=self.layers_per_block, in_channels=input_channel, out_channels=output_channel, - add_downsample=bool(add_spatial_downsample - or add_time_downsample), + add_downsample=bool(add_spatial_downsample or add_time_downsample), downsample_stride=downsample_stride, resnet_eps=1e-6, downsample_padding=0, @@ -111,20 +99,15 @@ class EncoderCausal3D(nn.Module): ) # out - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], - num_groups=norm_num_groups, - eps=1e-6) + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) self.conv_act = nn.SiLU() conv_out_channels = 2 * out_channels if double_z else out_channels - self.conv_out = CausalConv3d(block_out_channels[-1], - conv_out_channels, - kernel_size=3) + self.conv_out = CausalConv3d(block_out_channels[-1], conv_out_channels, kernel_size=3) def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor: r"""The forward method of the `EncoderCausal3D` class.""" - assert len( - sample.shape) == 5, "The input tensor should have 5 dimensions" + assert len(sample.shape) == 5, "The input tensor should have 5 dimensions" sample = self.conv_in(sample) @@ -165,10 +148,7 @@ class DecoderCausal3D(nn.Module): super().__init__() self.layers_per_block = layers_per_block - self.conv_in = CausalConv3d(in_channels, - block_out_channels[-1], - kernel_size=3, - stride=1) + self.conv_in = CausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1) self.mid_block = None self.up_blocks = nn.ModuleList([]) @@ -180,8 +160,7 @@ class DecoderCausal3D(nn.Module): resnet_eps=1e-6, resnet_act_fn=act_fn, output_scale_factor=1, - resnet_time_scale_shift="default" - if norm_type == "group" else norm_type, + resnet_time_scale_shift="default" if norm_type == "group" else norm_type, attention_head_dim=block_out_channels[-1], resnet_groups=norm_num_groups, temb_channels=temb_channels, @@ -195,25 +174,19 @@ class DecoderCausal3D(nn.Module): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 - num_spatial_upsample_layers = int( - np.log2(spatial_compression_ratio)) + num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio)) num_time_upsample_layers = int(np.log2(time_compression_ratio)) if time_compression_ratio == 4: add_spatial_upsample = bool(i < num_spatial_upsample_layers) - add_time_upsample = bool( - i >= len(block_out_channels) - 1 - num_time_upsample_layers - and not is_final_block) + add_time_upsample = bool(i >= len(block_out_channels) - 1 - num_time_upsample_layers + and not is_final_block) else: - raise ValueError( - f"Unsupported time_compression_ratio: {time_compression_ratio}." - ) + raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}.") - upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, - 1) + upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1) upsample_scale_factor_T = (2, ) if add_time_upsample else (1, ) - upsample_scale_factor = tuple(upsample_scale_factor_T + - upsample_scale_factor_HW) + upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW) up_block = get_up_block3d( up_block_type, num_layers=self.layers_per_block + 1, @@ -234,17 +207,11 @@ class DecoderCausal3D(nn.Module): # out if norm_type == "spatial": - self.conv_norm_out = SpatialNorm(block_out_channels[0], - temb_channels) + self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels) else: - self.conv_norm_out = nn.GroupNorm( - num_channels=block_out_channels[0], - num_groups=norm_num_groups, - eps=1e-6) + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6) self.conv_act = nn.SiLU() - self.conv_out = CausalConv3d(block_out_channels[0], - out_channels, - kernel_size=3) + self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3) self.gradient_checkpointing = False @@ -254,8 +221,7 @@ class DecoderCausal3D(nn.Module): latent_embeds: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: r"""The forward method of the `DecoderCausal3D` class.""" - assert len( - sample.shape) == 5, "The input tensor should have 5 dimensions." + assert len(sample.shape) == 5, "The input tensor should have 5 dimensions." sample = self.conv_in(sample) @@ -289,15 +255,12 @@ class DecoderCausal3D(nn.Module): ) else: # middle - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(self.mid_block), sample, - latent_embeds) + sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample, latent_embeds) sample = sample.to(upscale_dtype) # up for up_block in self.up_blocks: - sample = torch.utils.checkpoint.checkpoint( - create_custom_forward(up_block), sample, latent_embeds) + sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds) else: # middle sample = self.mid_block(sample, latent_embeds) @@ -334,14 +297,11 @@ class DiagonalGaussianDistribution(object): self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: - self.var = self.std = torch.zeros_like( - self.mean, - device=self.parameters.device, - dtype=self.parameters.dtype) + self.var = self.std = torch.zeros_like(self.mean, + device=self.parameters.device, + dtype=self.parameters.dtype) - def sample( - self, - generator: Optional[torch.Generator] = None) -> torch.FloatTensor: + def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor: # make sure sample is on the same device as the parameters and has same dtype sample = randn_tensor( self.mean.shape, @@ -364,20 +324,17 @@ class DiagonalGaussianDistribution(object): ) else: return 0.5 * torch.sum( - torch.pow(self.mean - other.mean, 2) / other.var + - self.var / other.var - 1.0 - self.logvar + other.logvar, + torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + + other.logvar, dim=reduce_dim, ) - def nll(self, - sample: torch.Tensor, - dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor: + def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor: if self.deterministic: return torch.Tensor([0.0]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( - logtwopi + self.logvar + - torch.pow(sample - self.mean, 2) / self.var, + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims, ) diff --git a/fastvideo/models/hunyuan_hf/modeling_hunyuan.py b/fastvideo/models/hunyuan_hf/modeling_hunyuan.py index a722ca636..4aa9edb31 100644 --- a/fastvideo/models/hunyuan_hf/modeling_hunyuan.py +++ b/fastvideo/models/hunyuan_hf/modeling_hunyuan.py @@ -21,29 +21,23 @@ from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin from diffusers.models.attention import FeedForward from diffusers.models.attention_processor import Attention, AttentionProcessor -from diffusers.models.embeddings import ( - CombinedTimestepGuidanceTextProjEmbeddings, - CombinedTimestepTextProjEmbeddings, get_1d_rotary_pos_embed) +from diffusers.models.embeddings import (CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, + get_1d_rotary_pos_embed) from diffusers.models.modeling_outputs import Transformer2DModelOutput from diffusers.models.modeling_utils import ModelMixin -from diffusers.models.normalization import (AdaLayerNormContinuous, - AdaLayerNormZero, - AdaLayerNormZeroSingle) -from diffusers.utils import (USE_PEFT_BACKEND, is_torch_version, logging, - scale_lora_layers, unscale_lora_layers) +from diffusers.models.normalization import (AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle) +from diffusers.utils import (USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers) from fastvideo.models.flash_attn_no_pad import flash_attn_no_pad from fastvideo.utils.communications import all_gather, all_to_all_4D -from fastvideo.utils.parallel_states import (get_sequence_parallel_state, - nccl_info) +from fastvideo.utils.parallel_states import (get_sequence_parallel_state, nccl_info) logger = logging.get_logger(__name__) # pylint: disable=invalid-name def shrink_head(encoder_state, dim): local_heads = encoder_state.shape[dim] // nccl_info.sp_size - return encoder_state.narrow(dim, nccl_info.rank_within_group * local_heads, - local_heads) + return encoder_state.narrow(dim, nccl_info.rank_within_group * local_heads, local_heads) class HunyuanVideoAttnProcessor2_0: @@ -51,8 +45,7 @@ class HunyuanVideoAttnProcessor2_0: def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError( - "HunyuanVideoAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0." - ) + "HunyuanVideoAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.") def __call__( self, @@ -66,8 +59,7 @@ class HunyuanVideoAttnProcessor2_0: sequence_length = hidden_states.size(1) encoder_sequence_length = encoder_hidden_states.size(1) if attn.add_q_proj is None and encoder_hidden_states is not None: - hidden_states = torch.cat([hidden_states, encoder_hidden_states], - dim=1) + hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1) # 1. QKV projections query = attn.to_q(hidden_states) @@ -96,18 +88,14 @@ class HunyuanVideoAttnProcessor2_0: if attn.add_q_proj is None and encoder_hidden_states is not None: query = torch.cat( [ - apply_rotary_emb( - query[:, :, :-encoder_hidden_states.shape[1]], - image_rotary_emb), + apply_rotary_emb(query[:, :, :-encoder_hidden_states.shape[1]], image_rotary_emb), query[:, :, -encoder_hidden_states.shape[1]:], ], dim=2, ) key = torch.cat( [ - apply_rotary_emb( - key[:, :, :-encoder_hidden_states.shape[1]], - image_rotary_emb), + apply_rotary_emb(key[:, :, :-encoder_hidden_states.shape[1]], image_rotary_emb), key[:, :, -encoder_hidden_states.shape[1]:], ], dim=2, @@ -122,16 +110,12 @@ class HunyuanVideoAttnProcessor2_0: encoder_key = attn.add_k_proj(encoder_hidden_states) encoder_value = attn.add_v_proj(encoder_hidden_states) - encoder_query = encoder_query.unflatten( - 2, (attn.heads, -1)).transpose(1, 2) - encoder_key = encoder_key.unflatten(2, (attn.heads, -1)).transpose( - 1, 2) - encoder_value = encoder_value.unflatten( - 2, (attn.heads, -1)).transpose(1, 2) + encoder_query = encoder_query.unflatten(2, (attn.heads, -1)).transpose(1, 2) + encoder_key = encoder_key.unflatten(2, (attn.heads, -1)).transpose(1, 2) + encoder_value = encoder_value.unflatten(2, (attn.heads, -1)).transpose(1, 2) if attn.norm_added_q is not None: - encoder_query = attn.norm_added_q(encoder_query).to( - encoder_value) + encoder_query = attn.norm_added_q(encoder_query).to(encoder_value) if attn.norm_added_k is not None: encoder_key = attn.norm_added_k(encoder_key).to(encoder_value) @@ -140,17 +124,10 @@ class HunyuanVideoAttnProcessor2_0: value = torch.cat([value, encoder_value], dim=2) if get_sequence_parallel_state(): - query_img, query_txt = query[:, :, : - sequence_length, :], query[:, :, - sequence_length:, :] - key_img, key_txt = key[:, :, : - sequence_length, :], key[:, :, - sequence_length:, :] - value_img, value_txt = value[:, :, : - sequence_length, :], value[:, :, - sequence_length:, :] - query_img = all_to_all_4D(query_img, scatter_dim=1, - gather_dim=2) # + query_img, query_txt = query[:, :, :sequence_length, :], query[:, :, sequence_length:, :] + key_img, key_txt = key[:, :, :sequence_length, :], key[:, :, sequence_length:, :] + value_img, value_txt = value[:, :, :sequence_length, :], value[:, :, sequence_length:, :] + query_img = all_to_all_4D(query_img, scatter_dim=1, gather_dim=2) # key_img = all_to_all_4D(key_img, scatter_dim=1, gather_dim=2) value_img = all_to_all_4D(value_img, scatter_dim=1, gather_dim=2) @@ -171,24 +148,15 @@ class HunyuanVideoAttnProcessor2_0: attention_mask = attention_mask[:, 0, :] seq_len = qkv.shape[1] attn_len = attention_mask.shape[1] - attention_mask = F.pad(attention_mask, (seq_len - attn_len, 0), - value=True) + attention_mask = F.pad(attention_mask, (seq_len - attn_len, 0), value=True) - hidden_states = flash_attn_no_pad(qkv, - attention_mask, - causal=False, - dropout_p=0.0, - softmax_scale=None) + hidden_states = flash_attn_no_pad(qkv, attention_mask, causal=False, dropout_p=0.0, softmax_scale=None) if get_sequence_parallel_state(): hidden_states, encoder_hidden_states = hidden_states.split_with_sizes( - (sequence_length * nccl_info.sp_size, encoder_sequence_length), - dim=1) - hidden_states = all_to_all_4D(hidden_states, - scatter_dim=1, - gather_dim=2) - encoder_hidden_states = all_gather(encoder_hidden_states, - dim=2).contiguous() + (sequence_length * nccl_info.sp_size, encoder_sequence_length), dim=1) + hidden_states = all_to_all_4D(hidden_states, scatter_dim=1, gather_dim=2) + encoder_hidden_states = all_gather(encoder_hidden_states, dim=2).contiguous() hidden_states = hidden_states.flatten(2, 3) hidden_states = hidden_states.to(query.dtype) encoder_hidden_states = encoder_hidden_states.flatten(2, 3) @@ -225,35 +193,26 @@ class HunyuanVideoPatchEmbed(nn.Module): ) -> None: super().__init__() - patch_size = (patch_size, patch_size, patch_size) if isinstance( - patch_size, int) else patch_size - self.proj = nn.Conv3d(in_chans, - embed_dim, - kernel_size=patch_size, - stride=patch_size) + patch_size = (patch_size, patch_size, patch_size) if isinstance(patch_size, int) else patch_size + self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.proj(hidden_states) - hidden_states = hidden_states.flatten(2).transpose(1, - 2) # BCFHW -> BNC + hidden_states = hidden_states.flatten(2).transpose(1, 2) # BCFHW -> BNC return hidden_states class HunyuanVideoAdaNorm(nn.Module): - def __init__(self, - in_features: int, - out_features: Optional[int] = None) -> None: + def __init__(self, in_features: int, out_features: Optional[int] = None) -> None: super().__init__() out_features = out_features or 2 * in_features self.linear = nn.Linear(in_features, out_features) self.nonlinearity = nn.SiLU() - def forward( - self, temb: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, - torch.Tensor]: + def forward(self, + temb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: temb = self.linear(self.nonlinearity(temb)) gate_msa, gate_mlp = temb.chunk(2, dim=1) gate_msa, gate_mlp = gate_msa.unsqueeze(1), gate_mlp.unsqueeze(1) @@ -274,9 +233,7 @@ class HunyuanVideoIndividualTokenRefinerBlock(nn.Module): hidden_size = num_attention_heads * attention_head_dim - self.norm1 = nn.LayerNorm(hidden_size, - elementwise_affine=True, - eps=1e-6) + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6) self.attn = Attention( query_dim=hidden_size, cross_attention_dim=None, @@ -285,13 +242,8 @@ class HunyuanVideoIndividualTokenRefinerBlock(nn.Module): bias=attention_bias, ) - self.norm2 = nn.LayerNorm(hidden_size, - elementwise_affine=True, - eps=1e-6) - self.ff = FeedForward(hidden_size, - mult=mlp_width_ratio, - activation_fn="linear-silu", - dropout=mlp_drop_rate) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6) + self.ff = FeedForward(hidden_size, mult=mlp_width_ratio, activation_fn="linear-silu", dropout=mlp_drop_rate) self.norm_out = HunyuanVideoAdaNorm(hidden_size, 2 * hidden_size) @@ -352,9 +304,7 @@ class HunyuanVideoIndividualTokenRefiner(nn.Module): batch_size = attention_mask.shape[0] seq_len = attention_mask.shape[1] attention_mask = attention_mask.to(hidden_states.device).bool() - self_attn_mask_1 = attention_mask.view(batch_size, 1, 1, - seq_len).repeat( - 1, 1, seq_len, 1) + self_attn_mask_1 = attention_mask.view(batch_size, 1, 1, seq_len).repeat(1, 1, seq_len, 1) self_attn_mask_2 = self_attn_mask_1.transpose(2, 3) self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool() self_attn_mask[:, :, :, 0] = True @@ -381,8 +331,8 @@ class HunyuanVideoTokenRefiner(nn.Module): hidden_size = num_attention_heads * attention_head_dim - self.time_text_embed = CombinedTimestepTextProjEmbeddings( - embedding_dim=hidden_size, pooled_projection_dim=in_channels) + self.time_text_embed = CombinedTimestepTextProjEmbeddings(embedding_dim=hidden_size, + pooled_projection_dim=in_channels) self.proj_in = nn.Linear(in_channels, hidden_size, bias=True) self.token_refiner = HunyuanVideoIndividualTokenRefiner( num_attention_heads=num_attention_heads, @@ -404,8 +354,7 @@ class HunyuanVideoTokenRefiner(nn.Module): else: original_dtype = hidden_states.dtype mask_float = attention_mask.float().unsqueeze(-1) - pooled_projections = (hidden_states * mask_float).sum( - dim=1) / mask_float.sum(dim=1) + pooled_projections = (hidden_states * mask_float).sum(dim=1) / mask_float.sum(dim=1) pooled_projections = pooled_projections.to(original_dtype) temb = self.time_text_embed(timestep, pooled_projections) @@ -417,11 +366,7 @@ class HunyuanVideoTokenRefiner(nn.Module): class HunyuanVideoRotaryPosEmbed(nn.Module): - def __init__(self, - patch_size: int, - patch_size_t: int, - rope_dim: List[int], - theta: float = 256.0) -> None: + def __init__(self, patch_size: int, patch_size_t: int, rope_dim: List[int], theta: float = 256.0) -> None: super().__init__() self.patch_size = patch_size @@ -432,8 +377,7 @@ class HunyuanVideoRotaryPosEmbed(nn.Module): def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: batch_size, num_channels, num_frames, height, width = hidden_states.shape rope_sizes = [ - num_frames * nccl_info.sp_size // self.patch_size_t, - height // self.patch_size, width // self.patch_size + num_frames * nccl_info.sp_size // self.patch_size_t, height // self.patch_size, width // self.patch_size ] axes_grids = [] @@ -441,26 +385,18 @@ class HunyuanVideoRotaryPosEmbed(nn.Module): # Note: The following line diverges from original behaviour. We create the grid on the device, whereas # original implementation creates it on CPU and then moves it to device. This results in numerical # differences in layerwise debugging outputs, but visually it is the same. - grid = torch.arange(0, - rope_sizes[i], - device=hidden_states.device, - dtype=torch.float32) + grid = torch.arange(0, rope_sizes[i], device=hidden_states.device, dtype=torch.float32) axes_grids.append(grid) grid = torch.meshgrid(*axes_grids, indexing="ij") # [W, H, T] grid = torch.stack(grid, dim=0) # [3, W, H, T] freqs = [] for i in range(3): - freq = get_1d_rotary_pos_embed(self.rope_dim[i], - grid[i].reshape(-1), - self.theta, - use_real=True) + freq = get_1d_rotary_pos_embed(self.rope_dim[i], grid[i].reshape(-1), self.theta, use_real=True) freqs.append(freq) - freqs_cos = torch.cat([f[0] for f in freqs], - dim=1) # (W * H * T, D / 2) - freqs_sin = torch.cat([f[1] for f in freqs], - dim=1) # (W * H * T, D / 2) + freqs_cos = torch.cat([f[0] for f in freqs], dim=1) # (W * H * T, D / 2) + freqs_sin = torch.cat([f[1] for f in freqs], dim=1) # (W * H * T, D / 2) return freqs_cos, freqs_sin @@ -505,8 +441,7 @@ class HunyuanVideoSingleTransformerBlock(nn.Module): image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ) -> torch.Tensor: text_seq_length = encoder_hidden_states.shape[1] - hidden_states = torch.cat([hidden_states, encoder_hidden_states], - dim=1) + hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1) residual = hidden_states @@ -554,8 +489,7 @@ class HunyuanVideoTransformerBlock(nn.Module): hidden_size = num_attention_heads * attention_head_dim self.norm1 = AdaLayerNormZero(hidden_size, norm_type="layer_norm") - self.norm1_context = AdaLayerNormZero(hidden_size, - norm_type="layer_norm") + self.norm1_context = AdaLayerNormZero(hidden_size, norm_type="layer_norm") self.attn = Attention( query_dim=hidden_size, @@ -571,19 +505,11 @@ class HunyuanVideoTransformerBlock(nn.Module): eps=1e-6, ) - self.norm2 = nn.LayerNorm(hidden_size, - elementwise_affine=False, - eps=1e-6) - self.ff = FeedForward(hidden_size, - mult=mlp_ratio, - activation_fn="gelu-approximate") + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.ff = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate") - self.norm2_context = nn.LayerNorm(hidden_size, - elementwise_affine=False, - eps=1e-6) - self.ff_context = FeedForward(hidden_size, - mult=mlp_ratio, - activation_fn="gelu-approximate") + self.norm2_context = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.ff_context = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate") def forward( self, @@ -594,8 +520,7 @@ class HunyuanVideoTransformerBlock(nn.Module): freqs_cis: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: # 1. Input normalization - norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( - hidden_states, emb=temb) + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb) norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context( encoder_hidden_states, emb=temb) @@ -609,30 +534,25 @@ class HunyuanVideoTransformerBlock(nn.Module): # 3. Modulation and residual connection hidden_states = hidden_states + attn_output * gate_msa.unsqueeze(1) - encoder_hidden_states = encoder_hidden_states + context_attn_output * c_gate_msa.unsqueeze( - 1) + encoder_hidden_states = encoder_hidden_states + context_attn_output * c_gate_msa.unsqueeze(1) norm_hidden_states = self.norm2(hidden_states) norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states) - norm_hidden_states = norm_hidden_states * ( - 1 + scale_mlp[:, None]) + shift_mlp[:, None] - norm_encoder_hidden_states = norm_encoder_hidden_states * ( - 1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] # 4. Feed-forward ff_output = self.ff(norm_hidden_states) context_ff_output = self.ff_context(norm_encoder_hidden_states) hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output - encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze( - 1) * context_ff_output + encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output return hidden_states, encoder_hidden_states -class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, - FromOriginalModelMixin): +class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin): r""" A Transformer model for video-like data used in [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo). @@ -699,26 +619,19 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, out_channels = out_channels or in_channels # 1. Latent and condition embedders - self.x_embedder = HunyuanVideoPatchEmbed( - (patch_size_t, patch_size, patch_size), in_channels, inner_dim) - self.context_embedder = HunyuanVideoTokenRefiner( - text_embed_dim, - num_attention_heads, - attention_head_dim, - num_layers=num_refiner_layers) - self.time_text_embed = CombinedTimestepGuidanceTextProjEmbeddings( - inner_dim, pooled_projection_dim) + self.x_embedder = HunyuanVideoPatchEmbed((patch_size_t, patch_size, patch_size), in_channels, inner_dim) + self.context_embedder = HunyuanVideoTokenRefiner(text_embed_dim, + num_attention_heads, + attention_head_dim, + num_layers=num_refiner_layers) + self.time_text_embed = CombinedTimestepGuidanceTextProjEmbeddings(inner_dim, pooled_projection_dim) # 2. RoPE - self.rope = HunyuanVideoRotaryPosEmbed(patch_size, patch_size_t, - rope_axes_dim, rope_theta) + self.rope = HunyuanVideoRotaryPosEmbed(patch_size, patch_size_t, rope_axes_dim, rope_theta) # 3. Dual stream transformer blocks self.transformer_blocks = nn.ModuleList([ - HunyuanVideoTransformerBlock(num_attention_heads, - attention_head_dim, - mlp_ratio=mlp_ratio, - qk_norm=qk_norm) + HunyuanVideoTransformerBlock(num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm) for _ in range(num_layers) ]) @@ -727,17 +640,12 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, HunyuanVideoSingleTransformerBlock(num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, - qk_norm=qk_norm) - for _ in range(num_single_layers) + qk_norm=qk_norm) for _ in range(num_single_layers) ]) # 5. Output projection - self.norm_out = AdaLayerNormContinuous(inner_dim, - inner_dim, - elementwise_affine=False, - eps=1e-6) - self.proj_out = nn.Linear( - inner_dim, patch_size_t * patch_size * patch_size * out_channels) + self.norm_out = AdaLayerNormContinuous(inner_dim, inner_dim, elementwise_affine=False, eps=1e-6) + self.proj_out = nn.Linear(inner_dim, patch_size_t * patch_size * patch_size * out_channels) self.gradient_checkpointing = False @@ -752,15 +660,12 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, # set recursively processors = {} - def fn_recursive_add_processors(name: str, module: torch.nn.Module, - processors: Dict[str, - AttentionProcessor]): + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "get_processor"): processors[f"{name}.processor"] = module.get_processor() for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, - processors) + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors @@ -770,9 +675,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, return processors # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor - def set_attn_processor(self, processor: Union[AttentionProcessor, - Dict[str, - AttentionProcessor]]): + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Sets the attention processor to use to compute attention. @@ -790,11 +693,9 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) + f" number of attention layers: {count}. Please make sure to pass {count} processor classes.") - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, - processor): + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) @@ -802,8 +703,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, - processor) + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) @@ -823,9 +723,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, return_dict: bool = True, ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: if guidance is None: - guidance = torch.tensor([6016.0], - device=hidden_states.device, - dtype=torch.bfloat16) + guidance = torch.tensor([6016.0], device=hidden_states.device, dtype=torch.bfloat16) if attention_kwargs is not None: attention_kwargs = attention_kwargs.copy() @@ -837,11 +735,8 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, # weight the lora layers by setting `lora_scale` for each PEFT layer scale_lora_layers(self, lora_scale) else: - if attention_kwargs is not None and attention_kwargs.get( - "scale", None) is not None: - logger.warning( - "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective." - ) + if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None: + logger.warning("Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective.") batch_size, num_channels, num_frames, height, width = hidden_states.shape p, p_t = self.config.patch_size, self.config.patch_size_t @@ -849,8 +744,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, post_patch_height = height // p post_patch_width = width // p - pooled_projections = encoder_hidden_states[:, 0, :self.config. - pooled_projection_dim] + pooled_projections = encoder_hidden_states[:, 0, :self.config.pooled_projection_dim] encoder_hidden_states = encoder_hidden_states[:, 1:] # 1. RoPE @@ -859,9 +753,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, # 2. Conditional embeddings temb = self.time_text_embed(timestep, guidance, pooled_projections) hidden_states = self.x_embedder(hidden_states) - encoder_hidden_states = self.context_embedder(encoder_hidden_states, - timestep, - encoder_attention_mask) + encoder_hidden_states = self.context_embedder(encoder_hidden_states, timestep, encoder_attention_mask) # 3. Attention mask preparation latent_sequence_length = hidden_states.shape[1] @@ -873,13 +765,11 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, device=hidden_states.device, dtype=torch.bool) # [B, N, N] - effective_condition_sequence_length = encoder_attention_mask.sum( - dim=1, dtype=torch.int) + effective_condition_sequence_length = encoder_attention_mask.sum(dim=1, dtype=torch.int) effective_sequence_length = latent_sequence_length + effective_condition_sequence_length for i in range(batch_size): - attention_mask[i, :effective_sequence_length[i], : - effective_sequence_length[i]] = True + attention_mask[i, :effective_sequence_length[i], :effective_sequence_length[i]] = True # 4. Transformer blocks if torch.is_grad_enabled() and self.gradient_checkpointing: @@ -894,9 +784,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, return custom_forward - ckpt_kwargs: Dict[str, Any] = { - "use_reentrant": False - } if is_torch_version(">=", "1.11.0") else {} + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} for block in self.transformer_blocks: hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint( @@ -922,23 +810,19 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, else: for block in self.transformer_blocks: - hidden_states, encoder_hidden_states = block( - hidden_states, encoder_hidden_states, temb, attention_mask, - image_rotary_emb) + hidden_states, encoder_hidden_states = block(hidden_states, encoder_hidden_states, temb, attention_mask, + image_rotary_emb) for block in self.single_transformer_blocks: - hidden_states, encoder_hidden_states = block( - hidden_states, encoder_hidden_states, temb, attention_mask, - image_rotary_emb) + hidden_states, encoder_hidden_states = block(hidden_states, encoder_hidden_states, temb, attention_mask, + image_rotary_emb) # 5. Output projection hidden_states = self.norm_out(hidden_states, temb) hidden_states = self.proj_out(hidden_states) - hidden_states = hidden_states.reshape(batch_size, - post_patch_num_frames, - post_patch_height, - post_patch_width, -1, p_t, p, p) + hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames, post_patch_height, post_patch_width, + -1, p_t, p, p) hidden_states = hidden_states.permute(0, 4, 1, 5, 2, 6, 3, 7) hidden_states = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3) diff --git a/fastvideo/models/hunyuan_hf/pipeline_hunyuan.py b/fastvideo/models/hunyuan_hf/pipeline_hunyuan.py index 18329e4d9..60a5dfbf2 100644 --- a/fastvideo/models/hunyuan_hf/pipeline_hunyuan.py +++ b/fastvideo/models/hunyuan_hf/pipeline_hunyuan.py @@ -20,8 +20,7 @@ import torch import torch.nn.functional as F from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.loaders import HunyuanVideoLoraLoaderMixin -from diffusers.models import (AutoencoderKLHunyuanVideo, - HunyuanVideoTransformer3DModel) +from diffusers.models import (AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel) from diffusers.pipelines.hunyuan_video.pipeline_output import \ HunyuanVideoPipelineOutput from diffusers.pipelines.pipeline_utils import DiffusionPipeline @@ -30,12 +29,10 @@ from diffusers.utils import logging, replace_example_docstring from diffusers.utils.torch_utils import randn_tensor from diffusers.video_processor import VideoProcessor from einops import rearrange -from transformers import (CLIPTextModel, CLIPTokenizer, LlamaModel, - LlamaTokenizerFast) +from transformers import (CLIPTextModel, CLIPTokenizer, LlamaModel, LlamaTokenizerFast) from fastvideo.utils.communications import all_gather -from fastvideo.utils.parallel_states import (get_sequence_parallel_state, - nccl_info) +from fastvideo.utils.parallel_states import (get_sequence_parallel_state, nccl_info) logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -66,14 +63,13 @@ EXAMPLE_DOC_STRING = """ """ DEFAULT_PROMPT_TEMPLATE = { - "template": - ("<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: " - "1. The main content and theme of the video." - "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." - "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." - "4. background environment, light, style and atmosphere." - "5. camera angles, movements, and transitions used in the video:<|eot_id|>" - "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"), + "template": ("<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"), "crop_start": 95, } @@ -112,28 +108,22 @@ def retrieve_timesteps( second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: - raise ValueError( - "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" - ) + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") if timesteps is not None: - accepts_timesteps = "timesteps" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys()) + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" timestep schedules. Please check whether you are using the correct scheduler." - ) + f" timestep schedules. Please check whether you are using the correct scheduler.") scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: - accept_sigmas = "sigmas" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys()) + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" sigmas schedules. Please check whether you are using the correct scheduler." - ) + f" sigmas schedules. Please check whether you are using the correct scheduler.") scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) @@ -195,13 +185,10 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): ) self.vae_scale_factor_temporal = (self.vae.temporal_compression_ratio - if hasattr(self, "vae") - and self.vae is not None else 4) + if hasattr(self, "vae") and self.vae is not None else 4) self.vae_scale_factor_spatial = (self.vae.spatial_compression_ratio - if hasattr(self, "vae") - and self.vae is not None else 8) - self.video_processor = VideoProcessor( - vae_scale_factor=self.vae_scale_factor_spatial) + if hasattr(self, "vae") and self.vae is not None else 8) + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) def _get_llama_prompt_embeds( self, @@ -263,12 +250,9 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): # duplicate text embeddings for each generation per prompt, using mps friendly method _, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) - prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, - seq_len, -1) - prompt_attention_mask = prompt_attention_mask.repeat( - 1, num_videos_per_prompt) - prompt_attention_mask = prompt_attention_mask.view( - batch_size * num_videos_per_prompt, seq_len) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) + prompt_attention_mask = prompt_attention_mask.repeat(1, num_videos_per_prompt) + prompt_attention_mask = prompt_attention_mask.view(batch_size * num_videos_per_prompt, seq_len) return prompt_embeds, prompt_attention_mask @@ -295,25 +279,17 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): ) text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer_2(prompt, - padding="longest", - return_tensors="pt").input_ids - if untruncated_ids.shape[-1] >= text_input_ids.shape[ - -1] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer_2.batch_decode( - untruncated_ids[:, max_sequence_length - 1:-1]) - logger.warning( - "The following part of your input was truncated because CLIP can only handle sequences up to" - f" {max_sequence_length} tokens: {removed_text}") + untruncated_ids = self.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1:-1]) + logger.warning("The following part of your input was truncated because CLIP can only handle sequences up to" + f" {max_sequence_length} tokens: {removed_text}") - prompt_embeds = self.text_encoder_2( - text_input_ids.to(device), - output_hidden_states=False).pooler_output + prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False).pooler_output # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt) - prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, - -1) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, -1) return prompt_embeds @@ -365,13 +341,10 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): prompt_template=None, ): if height % 16 != 0 or width % 16 != 0: - raise ValueError( - f"`height` and `width` have to be divisible by 16 but are {height} and {width}." - ) + raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.") - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs - for k in callback_on_step_end_tensor_inputs): + if callback_on_step_end_tensor_inputs is not None and not all(k in self._callback_tensor_inputs + for k in callback_on_step_end_tensor_inputs): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) @@ -386,28 +359,18 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): " only forward one of the two.") elif prompt is None and prompt_embeds is None: raise ValueError( - "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." - ) - elif prompt is not None and (not isinstance(prompt, str) - and not isinstance(prompt, list)): - raise ValueError( - f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" - ) - elif prompt_2 is not None and (not isinstance(prompt_2, str) - and not isinstance(prompt_2, list)): - raise ValueError( - f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}" - ) + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.") + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): + raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") if prompt_template is not None: if not isinstance(prompt_template, dict): - raise ValueError( - f"`prompt_template` has to be of type `dict` but is {type(prompt_template)}" - ) + raise ValueError(f"`prompt_template` has to be of type `dict` but is {type(prompt_template)}") if "template" not in prompt_template: raise ValueError( - f"`prompt_template` has to contain a key `template` but only found {prompt_template.keys()}" - ) + f"`prompt_template` has to contain a key `template` but only found {prompt_template.keys()}") def prepare_latents( self, @@ -418,8 +381,7 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): num_frames: int = 129, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, - generator: Optional[Union[torch.Generator, - List[torch.Generator]]] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, ) -> torch.Tensor: if latents is not None: @@ -435,13 +397,9 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) + f" size of {batch_size}. Make sure the batch size matches the length of the generators.") - latents = randn_tensor(shape, - generator=generator, - device=device, - dtype=dtype) + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) return latents def enable_vae_slicing(self): @@ -502,8 +460,7 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): sigmas: List[float] = None, guidance_scale: float = 6.0, num_videos_per_prompt: Optional[int] = 1, - generator: Optional[Union[torch.Generator, - List[torch.Generator]]] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, pooled_prompt_embeds: Optional[torch.Tensor] = None, @@ -511,8 +468,7 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): output_type: Optional[str] = "pil", return_dict: bool = True, attention_kwargs: Optional[Dict[str, Any]] = None, - callback_on_step_end: Optional[Union[Callable[[int, int, Dict], - None], PipelineCallback, + callback_on_step_end: Optional[Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE, @@ -591,8 +547,7 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. """ - if isinstance(callback_on_step_end, - (PipelineCallback, MultiPipelineCallbacks)): + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs # 1. Check inputs. Raise error if not correct @@ -640,8 +595,7 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype) # 4. Prepare timesteps - sigmas = np.linspace(1.0, 0.0, num_inference_steps + - 1)[:-1] if sigmas is None else sigmas + sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, @@ -651,8 +605,7 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): # 5. Prepare latent variables num_channels_latents = self.transformer.config.in_channels - num_latent_frames = (num_frames - - 1) // self.vae_scale_factor_temporal + 1 + num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 latents = self.prepare_latents( batch_size * num_videos_per_prompt, @@ -668,19 +621,14 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): # check sequence_parallel world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group if get_sequence_parallel_state(): - latents = rearrange(latents, - "b t (n s) h w -> b t n s h w", - n=world_size).contiguous() + latents = rearrange(latents, "b t (n s) h w -> b t n s h w", n=world_size).contiguous() latents = latents[:, :, rank, :, :, :] # 6. Prepare guidance condition - guidance = torch.tensor([guidance_scale] * latents.shape[0], - dtype=transformer_dtype, - device=device) * 1000.0 + guidance = torch.tensor([guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device) * 1000.0 # 7. Denoising loop - num_warmup_steps = len( - timesteps) - num_inference_steps * self.scheduler.order + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: @@ -694,17 +642,14 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): if pooled_prompt_embeds.shape[-1] != prompt_embeds.shape[-1]: pooled_prompt_embeds_padding = F.pad( pooled_prompt_embeds, - (0, prompt_embeds.shape[2] - - pooled_prompt_embeds.shape[1]), + (0, prompt_embeds.shape[2] - pooled_prompt_embeds.shape[1]), value=0, ).unsqueeze(1) - encoder_hidden_states = torch.cat( - [pooled_prompt_embeds_padding, prompt_embeds], dim=1) + encoder_hidden_states = torch.cat([pooled_prompt_embeds_padding, prompt_embeds], dim=1) noise_pred = self.transformer( hidden_states=latent_model_input, - encoder_hidden_states= - encoder_hidden_states, # [1, 257, 4096] + encoder_hidden_states=encoder_hidden_states, # [1, 257, 4096] timestep=timestep, encoder_attention_mask=prompt_attention_mask, guidance=guidance, @@ -713,37 +658,28 @@ class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): )[0] # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, - t, - latents, - return_dict=False)[0] + latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end( - self, i, t, callback_kwargs) + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop( - "prompt_embeds", prompt_embeds) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) # call the callback, if provided - if i == len(timesteps) - 1 or ( - (i + 1) > num_warmup_steps and - (i + 1) % self.scheduler.order == 0): + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if get_sequence_parallel_state(): latents = all_gather(latents, dim=2) if not output_type == "latent": - latents = latents.to( - self.vae.dtype) / self.vae.config.scaling_factor + latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor video = self.vae.decode(latents, return_dict=False)[0] - video = self.video_processor.postprocess_video( - video, output_type=output_type) + video = self.video_processor.postprocess_video(video, output_type=output_type) else: video = latents diff --git a/fastvideo/models/mochi_hf/convert_diffusers_to_mochi.py b/fastvideo/models/mochi_hf/convert_diffusers_to_mochi.py index 7382016d5..4249ba971 100644 --- a/fastvideo/models/mochi_hf/convert_diffusers_to_mochi.py +++ b/fastvideo/models/mochi_hf/convert_diffusers_to_mochi.py @@ -6,18 +6,9 @@ from safetensors.torch import save_file parser = argparse.ArgumentParser() parser.add_argument("--diffusers_path", required=True, type=str) -parser.add_argument("--transformer_path", - type=str, - default=None, - help="Path to save transformer model") -parser.add_argument("--vae_encoder_path", - type=str, - default=None, - help="Path to save VAE encoder model") -parser.add_argument("--vae_decoder_path", - type=str, - default=None, - help="Path to save VAE decoder model") +parser.add_argument("--transformer_path", type=str, default=None, help="Path to save transformer model") +parser.add_argument("--vae_encoder_path", type=str, default=None, help="Path to save VAE encoder model") +parser.add_argument("--vae_decoder_path", type=str, default=None, help="Path to save VAE decoder model") args = parser.parse_args() @@ -39,36 +30,22 @@ def convert_diffusers_transformer_to_mochi(state_dict): new_state_dict = {} # Convert patch_embed - new_state_dict["x_embedder.proj.weight"] = original_state_dict.pop( - "patch_embed.proj.weight") - new_state_dict["x_embedder.proj.bias"] = original_state_dict.pop( - "patch_embed.proj.bias") + new_state_dict["x_embedder.proj.weight"] = original_state_dict.pop("patch_embed.proj.weight") + new_state_dict["x_embedder.proj.bias"] = original_state_dict.pop("patch_embed.proj.bias") # Convert time_embed - new_state_dict["t_embedder.mlp.0.weight"] = original_state_dict.pop( - "time_embed.timestep_embedder.linear_1.weight") - new_state_dict["t_embedder.mlp.0.bias"] = original_state_dict.pop( - "time_embed.timestep_embedder.linear_1.bias") - new_state_dict["t_embedder.mlp.2.weight"] = original_state_dict.pop( - "time_embed.timestep_embedder.linear_2.weight") - new_state_dict["t_embedder.mlp.2.bias"] = original_state_dict.pop( - "time_embed.timestep_embedder.linear_2.bias") - new_state_dict["t5_y_embedder.to_kv.weight"] = original_state_dict.pop( - "time_embed.pooler.to_kv.weight") - new_state_dict["t5_y_embedder.to_kv.bias"] = original_state_dict.pop( - "time_embed.pooler.to_kv.bias") - new_state_dict["t5_y_embedder.to_q.weight"] = original_state_dict.pop( - "time_embed.pooler.to_q.weight") - new_state_dict["t5_y_embedder.to_q.bias"] = original_state_dict.pop( - "time_embed.pooler.to_q.bias") - new_state_dict["t5_y_embedder.to_out.weight"] = original_state_dict.pop( - "time_embed.pooler.to_out.weight") - new_state_dict["t5_y_embedder.to_out.bias"] = original_state_dict.pop( - "time_embed.pooler.to_out.bias") - new_state_dict["t5_yproj.weight"] = original_state_dict.pop( - "time_embed.caption_proj.weight") - new_state_dict["t5_yproj.bias"] = original_state_dict.pop( - "time_embed.caption_proj.bias") + new_state_dict["t_embedder.mlp.0.weight"] = original_state_dict.pop("time_embed.timestep_embedder.linear_1.weight") + new_state_dict["t_embedder.mlp.0.bias"] = original_state_dict.pop("time_embed.timestep_embedder.linear_1.bias") + new_state_dict["t_embedder.mlp.2.weight"] = original_state_dict.pop("time_embed.timestep_embedder.linear_2.weight") + new_state_dict["t_embedder.mlp.2.bias"] = original_state_dict.pop("time_embed.timestep_embedder.linear_2.bias") + new_state_dict["t5_y_embedder.to_kv.weight"] = original_state_dict.pop("time_embed.pooler.to_kv.weight") + new_state_dict["t5_y_embedder.to_kv.bias"] = original_state_dict.pop("time_embed.pooler.to_kv.bias") + new_state_dict["t5_y_embedder.to_q.weight"] = original_state_dict.pop("time_embed.pooler.to_q.weight") + new_state_dict["t5_y_embedder.to_q.bias"] = original_state_dict.pop("time_embed.pooler.to_q.bias") + new_state_dict["t5_y_embedder.to_out.weight"] = original_state_dict.pop("time_embed.pooler.to_out.weight") + new_state_dict["t5_y_embedder.to_out.bias"] = original_state_dict.pop("time_embed.pooler.to_out.bias") + new_state_dict["t5_yproj.weight"] = original_state_dict.pop("time_embed.caption_proj.weight") + new_state_dict["t5_yproj.bias"] = original_state_dict.pop("time_embed.caption_proj.bias") # Convert transformer blocks num_layers = 48 @@ -77,25 +54,19 @@ def convert_diffusers_transformer_to_mochi(state_dict): new_prefix = f"blocks.{i}." # norm1 - new_state_dict[new_prefix + "mod_x.weight"] = original_state_dict.pop( - block_prefix + "norm1.linear.weight") - new_state_dict[new_prefix + "mod_x.bias"] = original_state_dict.pop( - block_prefix + "norm1.linear.bias") + new_state_dict[new_prefix + "mod_x.weight"] = original_state_dict.pop(block_prefix + "norm1.linear.weight") + new_state_dict[new_prefix + "mod_x.bias"] = original_state_dict.pop(block_prefix + "norm1.linear.bias") if i < num_layers - 1: - new_state_dict[new_prefix + - "mod_y.weight"] = original_state_dict.pop( - block_prefix + "norm1_context.linear.weight") - new_state_dict[new_prefix + - "mod_y.bias"] = original_state_dict.pop( - block_prefix + "norm1_context.linear.bias") + new_state_dict[new_prefix + "mod_y.weight"] = original_state_dict.pop(block_prefix + + "norm1_context.linear.weight") + new_state_dict[new_prefix + "mod_y.bias"] = original_state_dict.pop(block_prefix + + "norm1_context.linear.bias") else: - new_state_dict[new_prefix + - "mod_y.weight"] = original_state_dict.pop( - block_prefix + "norm1_context.linear_1.weight") - new_state_dict[new_prefix + - "mod_y.bias"] = original_state_dict.pop( - block_prefix + "norm1_context.linear_1.bias") + new_state_dict[new_prefix + "mod_y.weight"] = original_state_dict.pop(block_prefix + + "norm1_context.linear_1.weight") + new_state_dict[new_prefix + "mod_y.bias"] = original_state_dict.pop(block_prefix + + "norm1_context.linear_1.bias") # Visual attention q = original_state_dict.pop(block_prefix + "attn1.to_q.weight") @@ -104,18 +75,13 @@ def convert_diffusers_transformer_to_mochi(state_dict): qkv_weight = torch.cat([q, k, v], dim=0) new_state_dict[new_prefix + "attn.qkv_x.weight"] = qkv_weight - new_state_dict[new_prefix + - "attn.q_norm_x.weight"] = original_state_dict.pop( - block_prefix + "attn1.norm_q.weight") - new_state_dict[new_prefix + - "attn.k_norm_x.weight"] = original_state_dict.pop( - block_prefix + "attn1.norm_k.weight") - new_state_dict[new_prefix + - "attn.proj_x.weight"] = original_state_dict.pop( - block_prefix + "attn1.to_out.0.weight") - new_state_dict[new_prefix + - "attn.proj_x.bias"] = original_state_dict.pop( - block_prefix + "attn1.to_out.0.bias") + new_state_dict[new_prefix + "attn.q_norm_x.weight"] = original_state_dict.pop(block_prefix + + "attn1.norm_q.weight") + new_state_dict[new_prefix + "attn.k_norm_x.weight"] = original_state_dict.pop(block_prefix + + "attn1.norm_k.weight") + new_state_dict[new_prefix + "attn.proj_x.weight"] = original_state_dict.pop(block_prefix + + "attn1.to_out.0.weight") + new_state_dict[new_prefix + "attn.proj_x.bias"] = original_state_dict.pop(block_prefix + "attn1.to_out.0.bias") # Context attention q = original_state_dict.pop(block_prefix + "attn1.add_q_proj.weight") @@ -124,46 +90,34 @@ def convert_diffusers_transformer_to_mochi(state_dict): qkv_weight = torch.cat([q, k, v], dim=0) new_state_dict[new_prefix + "attn.qkv_y.weight"] = qkv_weight - new_state_dict[new_prefix + - "attn.q_norm_y.weight"] = original_state_dict.pop( - block_prefix + "attn1.norm_added_q.weight") - new_state_dict[new_prefix + - "attn.k_norm_y.weight"] = original_state_dict.pop( - block_prefix + "attn1.norm_added_k.weight") + new_state_dict[new_prefix + "attn.q_norm_y.weight"] = original_state_dict.pop(block_prefix + + "attn1.norm_added_q.weight") + new_state_dict[new_prefix + "attn.k_norm_y.weight"] = original_state_dict.pop(block_prefix + + "attn1.norm_added_k.weight") if i < num_layers - 1: - new_state_dict[new_prefix + - "attn.proj_y.weight"] = original_state_dict.pop( - block_prefix + "attn1.to_add_out.weight") - new_state_dict[new_prefix + - "attn.proj_y.bias"] = original_state_dict.pop( - block_prefix + "attn1.to_add_out.bias") + new_state_dict[new_prefix + "attn.proj_y.weight"] = original_state_dict.pop(block_prefix + + "attn1.to_add_out.weight") + new_state_dict[new_prefix + "attn.proj_y.bias"] = original_state_dict.pop(block_prefix + + "attn1.to_add_out.bias") # MLP new_state_dict[new_prefix + "mlp_x.w1.weight"] = reverse_proj_gate( original_state_dict.pop(block_prefix + "ff.net.0.proj.weight")) - new_state_dict[new_prefix + - "mlp_x.w2.weight"] = original_state_dict.pop( - block_prefix + "ff.net.2.weight") + new_state_dict[new_prefix + "mlp_x.w2.weight"] = original_state_dict.pop(block_prefix + "ff.net.2.weight") if i < num_layers - 1: new_state_dict[new_prefix + "mlp_y.w1.weight"] = reverse_proj_gate( - original_state_dict.pop(block_prefix + - "ff_context.net.0.proj.weight")) - new_state_dict[new_prefix + - "mlp_y.w2.weight"] = original_state_dict.pop( - block_prefix + "ff_context.net.2.weight") + original_state_dict.pop(block_prefix + "ff_context.net.0.proj.weight")) + new_state_dict[new_prefix + "mlp_y.w2.weight"] = original_state_dict.pop(block_prefix + + "ff_context.net.2.weight") # Output layers - new_state_dict["final_layer.mod.weight"] = reverse_scale_shift( - original_state_dict.pop("norm_out.linear.weight"), dim=0) - new_state_dict["final_layer.mod.bias"] = reverse_scale_shift( - original_state_dict.pop("norm_out.linear.bias"), dim=0) - new_state_dict["final_layer.linear.weight"] = original_state_dict.pop( - "proj_out.weight") - new_state_dict["final_layer.linear.bias"] = original_state_dict.pop( - "proj_out.bias") + new_state_dict["final_layer.mod.weight"] = reverse_scale_shift(original_state_dict.pop("norm_out.linear.weight"), + dim=0) + new_state_dict["final_layer.mod.bias"] = reverse_scale_shift(original_state_dict.pop("norm_out.linear.bias"), dim=0) + new_state_dict["final_layer.linear.weight"] = original_state_dict.pop("proj_out.weight") + new_state_dict["final_layer.linear.bias"] = original_state_dict.pop("proj_out.bias") - new_state_dict["pos_frequencies"] = original_state_dict.pop( - "pos_frequencies") + new_state_dict["pos_frequencies"] = original_state_dict.pop("pos_frequencies") print("Remaining Keys:", original_state_dict.keys()) @@ -178,271 +132,182 @@ def convert_diffusers_vae_to_mochi(state_dict): # Convert encoder prefix = "encoder." - encoder_state_dict["layers.0.weight"] = original_state_dict.pop( - f"{prefix}proj_in.weight") - encoder_state_dict["layers.0.bias"] = original_state_dict.pop( - f"{prefix}proj_in.bias") + encoder_state_dict["layers.0.weight"] = original_state_dict.pop(f"{prefix}proj_in.weight") + encoder_state_dict["layers.0.bias"] = original_state_dict.pop(f"{prefix}proj_in.bias") # Convert block_in for i in range(3): - encoder_state_dict[ - f"layers.{i+1}.stack.0.weight"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight") - encoder_state_dict[ - f"layers.{i+1}.stack.0.bias"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias") - encoder_state_dict[ - f"layers.{i+1}.stack.2.weight"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.conv1.conv.weight") - encoder_state_dict[ - f"layers.{i+1}.stack.2.bias"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.conv1.conv.bias") - encoder_state_dict[ - f"layers.{i+1}.stack.3.weight"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight") - encoder_state_dict[ - f"layers.{i+1}.stack.3.bias"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias") - encoder_state_dict[ - f"layers.{i+1}.stack.5.weight"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.conv2.conv.weight") - encoder_state_dict[ - f"layers.{i+1}.stack.5.bias"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.conv2.conv.bias") + encoder_state_dict[f"layers.{i+1}.stack.0.weight"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight") + encoder_state_dict[f"layers.{i+1}.stack.0.bias"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias") + encoder_state_dict[f"layers.{i+1}.stack.2.weight"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.conv1.conv.weight") + encoder_state_dict[f"layers.{i+1}.stack.2.bias"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.conv1.conv.bias") + encoder_state_dict[f"layers.{i+1}.stack.3.weight"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight") + encoder_state_dict[f"layers.{i+1}.stack.3.bias"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias") + encoder_state_dict[f"layers.{i+1}.stack.5.weight"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.conv2.conv.weight") + encoder_state_dict[f"layers.{i+1}.stack.5.bias"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.conv2.conv.bias") # Convert down_blocks down_block_layers = [3, 4, 6] for block in range(3): - encoder_state_dict[ - f"layers.{block+4}.layers.0.weight"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.conv_in.conv.weight") - encoder_state_dict[ - f"layers.{block+4}.layers.0.bias"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.conv_in.conv.bias") + encoder_state_dict[f"layers.{block+4}.layers.0.weight"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.conv_in.conv.weight") + encoder_state_dict[f"layers.{block+4}.layers.0.bias"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.conv_in.conv.bias") for i in range(down_block_layers[block]): # Convert resnets - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.stack.0.weight"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight" - ) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.stack.0.bias"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.bias" - ) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.stack.2.weight"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.weight" - ) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.stack.2.bias"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias") - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.stack.3.weight"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight" - ) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.stack.3.bias"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.bias" - ) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.stack.5.weight"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.weight" - ) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.stack.5.bias"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.0.weight"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.0.bias"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.bias") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.2.weight"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.weight") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.2.bias"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.3.weight"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.3.bias"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.bias") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.5.weight"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.weight") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.stack.5.bias"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias") # Convert attentions - q = original_state_dict.pop( - f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight") - k = original_state_dict.pop( - f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight") - v = original_state_dict.pop( - f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight") + q = original_state_dict.pop(f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight") + k = original_state_dict.pop(f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight") + v = original_state_dict.pop(f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight") qkv_weight = torch.cat([q, k, v], dim=0) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.attn_block.attn.qkv.weight"] = qkv_weight + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.attn_block.attn.qkv.weight"] = qkv_weight - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.weight"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight" - ) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.bias"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias" - ) - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.attn_block.norm.weight"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight") - encoder_state_dict[ - f"layers.{block+4}.layers.{i+1}.attn_block.norm.bias"] = original_state_dict.pop( - f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.weight"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.bias"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.attn_block.norm.weight"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight") + encoder_state_dict[f"layers.{block+4}.layers.{i+1}.attn_block.norm.bias"] = original_state_dict.pop( + f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias") # Convert block_out for i in range(3): - encoder_state_dict[ - f"layers.{i+7}.stack.0.weight"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight") - encoder_state_dict[ - f"layers.{i+7}.stack.0.bias"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias") - encoder_state_dict[ - f"layers.{i+7}.stack.2.weight"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.conv1.conv.weight") - encoder_state_dict[ - f"layers.{i+7}.stack.2.bias"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.conv1.conv.bias") - encoder_state_dict[ - f"layers.{i+7}.stack.3.weight"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight") - encoder_state_dict[ - f"layers.{i+7}.stack.3.bias"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias") - encoder_state_dict[ - f"layers.{i+7}.stack.5.weight"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.conv2.conv.weight") - encoder_state_dict[ - f"layers.{i+7}.stack.5.bias"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.conv2.conv.bias") + encoder_state_dict[f"layers.{i+7}.stack.0.weight"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight") + encoder_state_dict[f"layers.{i+7}.stack.0.bias"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias") + encoder_state_dict[f"layers.{i+7}.stack.2.weight"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.conv1.conv.weight") + encoder_state_dict[f"layers.{i+7}.stack.2.bias"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.conv1.conv.bias") + encoder_state_dict[f"layers.{i+7}.stack.3.weight"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight") + encoder_state_dict[f"layers.{i+7}.stack.3.bias"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias") + encoder_state_dict[f"layers.{i+7}.stack.5.weight"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.conv2.conv.weight") + encoder_state_dict[f"layers.{i+7}.stack.5.bias"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.conv2.conv.bias") - q = original_state_dict.pop( - f"{prefix}block_out.attentions.{i}.to_q.weight") - k = original_state_dict.pop( - f"{prefix}block_out.attentions.{i}.to_k.weight") - v = original_state_dict.pop( - f"{prefix}block_out.attentions.{i}.to_v.weight") + q = original_state_dict.pop(f"{prefix}block_out.attentions.{i}.to_q.weight") + k = original_state_dict.pop(f"{prefix}block_out.attentions.{i}.to_k.weight") + v = original_state_dict.pop(f"{prefix}block_out.attentions.{i}.to_v.weight") qkv_weight = torch.cat([q, k, v], dim=0) - encoder_state_dict[ - f"layers.{i+7}.attn_block.attn.qkv.weight"] = qkv_weight + encoder_state_dict[f"layers.{i+7}.attn_block.attn.qkv.weight"] = qkv_weight - encoder_state_dict[ - f"layers.{i+7}.attn_block.attn.out.weight"] = original_state_dict.pop( - f"{prefix}block_out.attentions.{i}.to_out.0.weight") - encoder_state_dict[ - f"layers.{i+7}.attn_block.attn.out.bias"] = original_state_dict.pop( - f"{prefix}block_out.attentions.{i}.to_out.0.bias") - encoder_state_dict[ - f"layers.{i+7}.attn_block.norm.weight"] = original_state_dict.pop( - f"{prefix}block_out.norms.{i}.norm_layer.weight") - encoder_state_dict[ - f"layers.{i+7}.attn_block.norm.bias"] = original_state_dict.pop( - f"{prefix}block_out.norms.{i}.norm_layer.bias") + encoder_state_dict[f"layers.{i+7}.attn_block.attn.out.weight"] = original_state_dict.pop( + f"{prefix}block_out.attentions.{i}.to_out.0.weight") + encoder_state_dict[f"layers.{i+7}.attn_block.attn.out.bias"] = original_state_dict.pop( + f"{prefix}block_out.attentions.{i}.to_out.0.bias") + encoder_state_dict[f"layers.{i+7}.attn_block.norm.weight"] = original_state_dict.pop( + f"{prefix}block_out.norms.{i}.norm_layer.weight") + encoder_state_dict[f"layers.{i+7}.attn_block.norm.bias"] = original_state_dict.pop( + f"{prefix}block_out.norms.{i}.norm_layer.bias") # Convert output layers - encoder_state_dict["output_norm.weight"] = original_state_dict.pop( - f"{prefix}norm_out.norm_layer.weight") - encoder_state_dict["output_norm.bias"] = original_state_dict.pop( - f"{prefix}norm_out.norm_layer.bias") - encoder_state_dict["output_proj.weight"] = original_state_dict.pop( - f"{prefix}proj_out.weight") + encoder_state_dict["output_norm.weight"] = original_state_dict.pop(f"{prefix}norm_out.norm_layer.weight") + encoder_state_dict["output_norm.bias"] = original_state_dict.pop(f"{prefix}norm_out.norm_layer.bias") + encoder_state_dict["output_proj.weight"] = original_state_dict.pop(f"{prefix}proj_out.weight") # Convert decoder prefix = "decoder." - decoder_state_dict["blocks.0.0.weight"] = original_state_dict.pop( - f"{prefix}conv_in.weight") - decoder_state_dict["blocks.0.0.bias"] = original_state_dict.pop( - f"{prefix}conv_in.bias") + decoder_state_dict["blocks.0.0.weight"] = original_state_dict.pop(f"{prefix}conv_in.weight") + decoder_state_dict["blocks.0.0.bias"] = original_state_dict.pop(f"{prefix}conv_in.bias") # Convert block_in for i in range(3): - decoder_state_dict[ - f"blocks.0.{i+1}.stack.0.weight"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight") - decoder_state_dict[ - f"blocks.0.{i+1}.stack.0.bias"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias") - decoder_state_dict[ - f"blocks.0.{i+1}.stack.2.weight"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.conv1.conv.weight") - decoder_state_dict[ - f"blocks.0.{i+1}.stack.2.bias"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.conv1.conv.bias") - decoder_state_dict[ - f"blocks.0.{i+1}.stack.3.weight"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight") - decoder_state_dict[ - f"blocks.0.{i+1}.stack.3.bias"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias") - decoder_state_dict[ - f"blocks.0.{i+1}.stack.5.weight"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.conv2.conv.weight") - decoder_state_dict[ - f"blocks.0.{i+1}.stack.5.bias"] = original_state_dict.pop( - f"{prefix}block_in.resnets.{i}.conv2.conv.bias") + decoder_state_dict[f"blocks.0.{i+1}.stack.0.weight"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight") + decoder_state_dict[f"blocks.0.{i+1}.stack.0.bias"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias") + decoder_state_dict[f"blocks.0.{i+1}.stack.2.weight"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.conv1.conv.weight") + decoder_state_dict[f"blocks.0.{i+1}.stack.2.bias"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.conv1.conv.bias") + decoder_state_dict[f"blocks.0.{i+1}.stack.3.weight"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight") + decoder_state_dict[f"blocks.0.{i+1}.stack.3.bias"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias") + decoder_state_dict[f"blocks.0.{i+1}.stack.5.weight"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.conv2.conv.weight") + decoder_state_dict[f"blocks.0.{i+1}.stack.5.bias"] = original_state_dict.pop( + f"{prefix}block_in.resnets.{i}.conv2.conv.bias") # Convert up_blocks up_block_layers = [6, 4, 3] for block in range(3): for i in range(up_block_layers[block]): - decoder_state_dict[ - f"blocks.{block+1}.blocks.{i}.stack.0.weight"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight" - ) - decoder_state_dict[ - f"blocks.{block+1}.blocks.{i}.stack.0.bias"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias" - ) - decoder_state_dict[ - f"blocks.{block+1}.blocks.{i}.stack.2.weight"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight") - decoder_state_dict[ - f"blocks.{block+1}.blocks.{i}.stack.2.bias"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias") - decoder_state_dict[ - f"blocks.{block+1}.blocks.{i}.stack.3.weight"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight" - ) - decoder_state_dict[ - f"blocks.{block+1}.blocks.{i}.stack.3.bias"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias" - ) - decoder_state_dict[ - f"blocks.{block+1}.blocks.{i}.stack.5.weight"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight") - decoder_state_dict[ - f"blocks.{block+1}.blocks.{i}.stack.5.bias"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias") - decoder_state_dict[ - f"blocks.{block+1}.proj.weight"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.proj.weight") - decoder_state_dict[ - f"blocks.{block+1}.proj.bias"] = original_state_dict.pop( - f"{prefix}up_blocks.{block}.proj.bias") + decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.0.weight"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight") + decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.0.bias"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias") + decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.2.weight"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight") + decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.2.bias"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias") + decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.3.weight"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight") + decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.3.bias"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias") + decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.5.weight"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight") + decoder_state_dict[f"blocks.{block+1}.blocks.{i}.stack.5.bias"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias") + decoder_state_dict[f"blocks.{block+1}.proj.weight"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.proj.weight") + decoder_state_dict[f"blocks.{block+1}.proj.bias"] = original_state_dict.pop( + f"{prefix}up_blocks.{block}.proj.bias") # Convert block_out for i in range(3): - decoder_state_dict[ - f"blocks.4.{i}.stack.0.weight"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight") - decoder_state_dict[ - f"blocks.4.{i}.stack.0.bias"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias") - decoder_state_dict[ - f"blocks.4.{i}.stack.2.weight"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.conv1.conv.weight") - decoder_state_dict[ - f"blocks.4.{i}.stack.2.bias"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.conv1.conv.bias") - decoder_state_dict[ - f"blocks.4.{i}.stack.3.weight"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight") - decoder_state_dict[ - f"blocks.4.{i}.stack.3.bias"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias") - decoder_state_dict[ - f"blocks.4.{i}.stack.5.weight"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.conv2.conv.weight") - decoder_state_dict[ - f"blocks.4.{i}.stack.5.bias"] = original_state_dict.pop( - f"{prefix}block_out.resnets.{i}.conv2.conv.bias") + decoder_state_dict[f"blocks.4.{i}.stack.0.weight"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight") + decoder_state_dict[f"blocks.4.{i}.stack.0.bias"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias") + decoder_state_dict[f"blocks.4.{i}.stack.2.weight"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.conv1.conv.weight") + decoder_state_dict[f"blocks.4.{i}.stack.2.bias"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.conv1.conv.bias") + decoder_state_dict[f"blocks.4.{i}.stack.3.weight"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight") + decoder_state_dict[f"blocks.4.{i}.stack.3.bias"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias") + decoder_state_dict[f"blocks.4.{i}.stack.5.weight"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.conv2.conv.weight") + decoder_state_dict[f"blocks.4.{i}.stack.5.bias"] = original_state_dict.pop( + f"{prefix}block_out.resnets.{i}.conv2.conv.bias") # Convert output layers - decoder_state_dict["output_proj.weight"] = original_state_dict.pop( - f"{prefix}proj_out.weight") - decoder_state_dict["output_proj.bias"] = original_state_dict.pop( - f"{prefix}proj_out.bias") + decoder_state_dict["output_proj.weight"] = original_state_dict.pop(f"{prefix}proj_out.weight") + decoder_state_dict["output_proj.bias"] = original_state_dict.pop(f"{prefix}proj_out.bias") return encoder_state_dict, decoder_state_dict @@ -469,8 +334,7 @@ def main(args): ensure_directory_exists(transformer_path) print("Converting transformer model...") - transformer_state_dict = convert_diffusers_transformer_to_mochi( - pipe.transformer.state_dict()) + transformer_state_dict = convert_diffusers_transformer_to_mochi(pipe.transformer.state_dict()) save_file(transformer_state_dict, transformer_path) print(f"Saved transformer to {transformer_path}") @@ -482,8 +346,7 @@ def main(args): ensure_directory_exists(decoder_path) print("Converting VAE models...") - encoder_state_dict, decoder_state_dict = convert_diffusers_vae_to_mochi( - pipe.vae.state_dict()) + encoder_state_dict, decoder_state_dict = convert_diffusers_vae_to_mochi(pipe.vae.state_dict()) save_file(encoder_state_dict, encoder_path) print(f"Saved VAE encoder to {encoder_path}") @@ -491,9 +354,7 @@ def main(args): save_file(decoder_state_dict, decoder_path) print(f"Saved VAE decoder to {decoder_path}") elif args.vae_encoder_path or args.vae_decoder_path: - print( - "Warning: Both VAE encoder and decoder paths must be specified to convert VAE models." - ) + print("Warning: Both VAE encoder and decoder paths must be specified to convert VAE models.") if __name__ == "__main__": diff --git a/fastvideo/models/mochi_hf/modeling_mochi.py b/fastvideo/models/mochi_hf/modeling_mochi.py index 330f31a57..5e6588b6d 100644 --- a/fastvideo/models/mochi_hf/modeling_mochi.py +++ b/fastvideo/models/mochi_hf/modeling_mochi.py @@ -21,22 +21,18 @@ from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.loaders import PeftAdapterMixin from diffusers.models.attention import FeedForward as HF_FeedForward from diffusers.models.attention_processor import Attention -from diffusers.models.embeddings import (MochiCombinedTimestepCaptionEmbedding, - PatchEmbed) +from diffusers.models.embeddings import (MochiCombinedTimestepCaptionEmbedding, PatchEmbed) from diffusers.models.modeling_utils import ModelMixin from diffusers.models.normalization import AdaLayerNormContinuous -from diffusers.utils import (USE_PEFT_BACKEND, is_torch_version, logging, - scale_lora_layers, unscale_lora_layers) +from diffusers.utils import (USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers) from diffusers.utils.torch_utils import maybe_allow_in_graph from liger_kernel.ops.swiglu import LigerSiLUMulFunction from fastvideo.models.flash_attn_no_pad import flash_attn_no_pad -from fastvideo.models.mochi_hf.norm import (MochiLayerNormContinuous, - MochiModulatedRMSNorm, - MochiRMSNorm, MochiRMSNormZero) +from fastvideo.models.mochi_hf.norm import (MochiLayerNormContinuous, MochiModulatedRMSNorm, MochiRMSNorm, + MochiRMSNormZero) from fastvideo.utils.communications import all_gather, all_to_all_4D -from fastvideo.utils.parallel_states import (get_sequence_parallel_state, - nccl_info) +from fastvideo.utils.parallel_states import (get_sequence_parallel_state, nccl_info) logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -54,8 +50,7 @@ class FeedForward(HF_FeedForward): inner_dim=None, bias: bool = True, ): - super().__init__(dim, dim_out, mult, dropout, activation_fn, - final_dropout, inner_dim, bias) + super().__init__(dim, dim_out, mult, dropout, activation_fn, final_dropout, inner_dim, bias) assert activation_fn == "swiglu" def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: @@ -100,26 +95,17 @@ class MochiAttention(nn.Module): self.to_k = nn.Linear(query_dim, self.inner_dim, bias=bias) self.to_v = nn.Linear(query_dim, self.inner_dim, bias=bias) - self.add_k_proj = nn.Linear(added_kv_proj_dim, - self.inner_dim, - bias=added_proj_bias) - self.add_v_proj = nn.Linear(added_kv_proj_dim, - self.inner_dim, - bias=added_proj_bias) + self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias) + self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias) if self.context_pre_only is not None: - self.add_q_proj = nn.Linear(added_kv_proj_dim, - self.inner_dim, - bias=added_proj_bias) + self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias) self.to_out = nn.ModuleList([]) - self.to_out.append( - nn.Linear(self.inner_dim, self.out_dim, bias=out_bias)) + self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias)) self.to_out.append(nn.Dropout(dropout)) if not self.context_pre_only: - self.to_add_out = nn.Linear(self.inner_dim, - self.out_context_dim, - bias=out_bias) + self.to_add_out = nn.Linear(self.inner_dim, self.out_context_dim, bias=out_bias) self.processor = processor @@ -144,9 +130,7 @@ class MochiAttnProcessor2_0: def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError( - "MochiAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0." - ) + raise ImportError("MochiAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.") def __call__( self, @@ -198,9 +182,7 @@ class MochiAttnProcessor2_0: def shrink_head(encoder_state, dim): local_heads = encoder_state.shape[dim] // nccl_info.sp_size - return encoder_state.narrow( - dim, nccl_info.rank_within_group * local_heads, - local_heads) + return encoder_state.narrow(dim, nccl_info.rank_within_group * local_heads, local_heads) encoder_query = shrink_head(encoder_query, dim=2) encoder_key = shrink_head(encoder_key, dim=2) @@ -241,11 +223,7 @@ class MochiAttnProcessor2_0: attn_mask = encoder_attention_mask[:, :].bool() attn_mask = F.pad(attn_mask, (sequence_length, 0), value=True) - hidden_states = flash_attn_no_pad(qkv, - attn_mask, - causal=False, - dropout_p=0.0, - softmax_scale=None) + hidden_states = flash_attn_no_pad(qkv, attn_mask, causal=False, dropout_p=0.0, softmax_scale=None) # hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask = None, dropout_p=0.0, is_causal=False) @@ -258,11 +236,8 @@ class MochiAttnProcessor2_0: hidden_states, encoder_hidden_states = hidden_states.split_with_sizes( (sequence_length, encoder_sequence_length), dim=1) # B, S, H, D - hidden_states = all_to_all_4D(hidden_states, - scatter_dim=1, - gather_dim=2) - encoder_hidden_states = all_gather(encoder_hidden_states, - dim=2).contiguous() + hidden_states = all_to_all_4D(hidden_states, scatter_dim=1, gather_dim=2) + encoder_hidden_states = all_gather(encoder_hidden_states, dim=2).contiguous() hidden_states = hidden_states.flatten(2, 3) hidden_states = hidden_states.to(query.dtype) encoder_hidden_states = encoder_hidden_states.flatten(2, 3) @@ -324,16 +299,10 @@ class MochiTransformerBlock(nn.Module): self.ff_inner_dim = (4 * dim * 2) // 3 self.ff_context_inner_dim = (4 * pooled_projection_dim * 2) // 3 - self.norm1 = MochiRMSNormZero(dim, - 4 * dim, - eps=eps, - elementwise_affine=False) + self.norm1 = MochiRMSNormZero(dim, 4 * dim, eps=eps, elementwise_affine=False) if not context_pre_only: - self.norm1_context = MochiRMSNormZero(dim, - 4 * pooled_projection_dim, - eps=eps, - elementwise_affine=False) + self.norm1_context = MochiRMSNormZero(dim, 4 * pooled_projection_dim, eps=eps, elementwise_affine=False) else: self.norm1_context = MochiLayerNormContinuous( embedding_dim=pooled_projection_dim, @@ -357,17 +326,12 @@ class MochiTransformerBlock(nn.Module): # TODO(aryan): norm_context layers are not needed when `context_pre_only` is True self.norm2 = MochiModulatedRMSNorm(eps=eps) - self.norm2_context = (MochiModulatedRMSNorm( - eps=eps) if not self.context_pre_only else None) + self.norm2_context = (MochiModulatedRMSNorm(eps=eps) if not self.context_pre_only else None) self.norm3 = MochiModulatedRMSNorm(eps) - self.norm3_context = (MochiModulatedRMSNorm( - eps=eps) if not self.context_pre_only else None) + self.norm3_context = (MochiModulatedRMSNorm(eps=eps) if not self.context_pre_only else None) - self.ff = FeedForward(dim, - inner_dim=self.ff_inner_dim, - activation_fn=activation_fn, - bias=False) + self.ff = FeedForward(dim, inner_dim=self.ff_inner_dim, activation_fn=activation_fn, bias=False) self.ff_context = None if not context_pre_only: self.ff_context = FeedForward( @@ -389,8 +353,7 @@ class MochiTransformerBlock(nn.Module): image_rotary_emb: Optional[torch.Tensor] = None, output_attn=False, ) -> Tuple[torch.Tensor, torch.Tensor]: - norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1( - hidden_states, temb) + norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) if not self.context_pre_only: ( @@ -400,8 +363,7 @@ class MochiTransformerBlock(nn.Module): enc_gate_mlp, ) = self.norm1_context(encoder_hidden_states, temb) else: - norm_encoder_hidden_states = self.norm1_context( - encoder_hidden_states, temb) + norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb) attn_hidden_states, context_attn_hidden_states = self.attn1( hidden_states=norm_hidden_states, @@ -410,28 +372,21 @@ class MochiTransformerBlock(nn.Module): encoder_attention_mask=encoder_attention_mask, ) - hidden_states = hidden_states + self.norm2( - attn_hidden_states, - torch.tanh(gate_msa).unsqueeze(1)) - norm_hidden_states = self.norm3( - hidden_states, (1 + scale_mlp.unsqueeze(1).to(torch.float32))) + hidden_states = hidden_states + self.norm2(attn_hidden_states, torch.tanh(gate_msa).unsqueeze(1)) + norm_hidden_states = self.norm3(hidden_states, (1 + scale_mlp.unsqueeze(1).to(torch.float32))) ff_output = self.ff(norm_hidden_states) - hidden_states = hidden_states + self.norm4( - ff_output, - torch.tanh(gate_mlp).unsqueeze(1)) + hidden_states = hidden_states + self.norm4(ff_output, torch.tanh(gate_mlp).unsqueeze(1)) if not self.context_pre_only: - encoder_hidden_states = encoder_hidden_states + self.norm2_context( - context_attn_hidden_states, - torch.tanh(enc_gate_msa).unsqueeze(1)) + encoder_hidden_states = encoder_hidden_states + self.norm2_context(context_attn_hidden_states, + torch.tanh(enc_gate_msa).unsqueeze(1)) norm_encoder_hidden_states = self.norm3_context( encoder_hidden_states, (1 + enc_scale_mlp.unsqueeze(1).to(torch.float32)), ) context_ff_output = self.ff_context(norm_encoder_hidden_states) - encoder_hidden_states = encoder_hidden_states + self.norm4_context( - context_ff_output, - torch.tanh(enc_gate_mlp).unsqueeze(1)) + encoder_hidden_states = encoder_hidden_states + self.norm4_context(context_ff_output, + torch.tanh(enc_gate_mlp).unsqueeze(1)) if not output_attn: attn_hidden_states = None @@ -455,11 +410,7 @@ class MochiRoPE(nn.Module): self.target_area = base_height * base_width def _centers(self, start, stop, num, device, dtype) -> torch.Tensor: - edges = torch.linspace(start, - stop, - num + 1, - device=device, - dtype=dtype) + edges = torch.linspace(start, stop, num + 1, device=device, dtype=dtype) return (edges[:-1] + edges[1:]) / 2 def _get_positions( @@ -471,21 +422,16 @@ class MochiRoPE(nn.Module): dtype: Optional[torch.dtype] = None, ) -> torch.Tensor: scale = (self.target_area / (height * width))**0.5 - t = torch.arange(num_frames * nccl_info.sp_size, - device=device, - dtype=dtype) - h = self._centers(-height * scale / 2, height * scale / 2, height, - device, dtype) - w = self._centers(-width * scale / 2, width * scale / 2, width, device, - dtype) + t = torch.arange(num_frames * nccl_info.sp_size, device=device, dtype=dtype) + h = self._centers(-height * scale / 2, height * scale / 2, height, device, dtype) + w = self._centers(-width * scale / 2, width * scale / 2, width, device, dtype) grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij") positions = torch.stack([grid_t, grid_h, grid_w], dim=-1).view(-1, 3) return positions - def _create_rope(self, freqs: torch.Tensor, - pos: torch.Tensor) -> torch.Tensor: + def _create_rope(self, freqs: torch.Tensor, pos: torch.Tensor) -> torch.Tensor: with torch.autocast(freqs.device.type, enabled=False): # Always run ROPE freqs computation in FP32 freqs = torch.einsum( @@ -578,8 +524,7 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin): num_attention_heads=8, ) - self.pos_frequencies = nn.Parameter( - torch.full((3, num_attention_heads, attention_head_dim // 2), 0.0)) + self.pos_frequencies = nn.Parameter(torch.full((3, num_attention_heads, attention_head_dim // 2), 0.0)) self.rope = MochiRoPE() self.transformer_blocks = nn.ModuleList([ @@ -601,8 +546,7 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin): eps=1e-6, norm_type="layer_norm", ) - self.proj_out = nn.Linear(inner_dim, - patch_size * patch_size * out_channels) + self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels) self.gradient_checkpointing = False @@ -621,8 +565,7 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin): attention_kwargs: Optional[Dict[str, Any]] = None, return_dict: bool = False, ) -> torch.Tensor: - assert (return_dict is False - ), "return_dict is not supported in MochiTransformer3DModel" + assert (return_dict is False), "return_dict is not supported in MochiTransformer3DModel" if attention_kwargs is not None: attention_kwargs = attention_kwargs.copy() @@ -634,11 +577,8 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin): # weight the lora layers by setting `lora_scale` for each PEFT layer scale_lora_layers(self, lora_scale) else: - if (attention_kwargs is not None - and attention_kwargs.get("scale", None) is not None): - logger.warning( - "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective." - ) + if (attention_kwargs is not None and attention_kwargs.get("scale", None) is not None): + logger.warning("Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective.") batch_size, num_channels, num_frames, height, width = hidden_states.shape p = self.config.patch_size @@ -656,8 +596,7 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin): hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1) hidden_states = self.patch_embed(hidden_states) - hidden_states = hidden_states.unflatten(0, (batch_size, -1)).flatten( - 1, 2) + hidden_states = hidden_states.unflatten(0, (batch_size, -1)).flatten(1, 2) image_rotary_emb = self.rope( self.pos_frequencies, @@ -678,9 +617,7 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin): return custom_forward - ckpt_kwargs: Dict[str, Any] = ({ - "use_reentrant": False - } if is_torch_version(">=", "1.11.0") else {}) + ckpt_kwargs: Dict[str, Any] = ({"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}) ( hidden_states, encoder_hidden_states, @@ -710,12 +647,9 @@ class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin): hidden_states = self.norm_out(hidden_states, temb) hidden_states = self.proj_out(hidden_states) - hidden_states = hidden_states.reshape(batch_size, num_frames, - post_patch_height, - post_patch_width, p, p, -1) + hidden_states = hidden_states.reshape(batch_size, num_frames, post_patch_height, post_patch_width, p, p, -1) hidden_states = hidden_states.permute(0, 6, 1, 2, 4, 3, 5) - output = hidden_states.reshape(batch_size, -1, num_frames, height, - width) + output = hidden_states.reshape(batch_size, -1, num_frames, height, width) if USE_PEFT_BACKEND: # remove `lora_scale` from each PEFT layer diff --git a/fastvideo/models/mochi_hf/norm.py b/fastvideo/models/mochi_hf/norm.py index f8fe455e3..d91bb95f1 100644 --- a/fastvideo/models/mochi_hf/norm.py +++ b/fastvideo/models/mochi_hf/norm.py @@ -78,9 +78,7 @@ class MochiLayerNormContinuous(nn.Module): # AdaLN self.silu = nn.SiLU() - self.linear_1 = nn.Linear(conditioning_embedding_dim, - embedding_dim, - bias=bias) + self.linear_1 = nn.Linear(conditioning_embedding_dim, embedding_dim, bias=bias) self.norm = MochiModulatedRMSNorm(eps=eps) def forward( @@ -117,16 +115,14 @@ class MochiRMSNormZero(nn.Module): self.linear = nn.Linear(embedding_dim, hidden_dim) self.norm = MochiModulatedRMSNorm(eps=eps) - def forward( - self, hidden_states: torch.Tensor, emb: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + def forward(self, hidden_states: torch.Tensor, + emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: hidden_states_dtype = hidden_states.dtype emb = self.linear(self.silu(emb)) scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1) - hidden_states = self.norm(hidden_states, - (1 + scale_msa[:, None].to(torch.float32))) + hidden_states = self.norm(hidden_states, (1 + scale_msa[:, None].to(torch.float32))) hidden_states = hidden_states.to(hidden_states_dtype) return hidden_states, gate_msa, scale_mlp, gate_mlp diff --git a/fastvideo/models/mochi_hf/pipeline_mochi.py b/fastvideo/models/mochi_hf/pipeline_mochi.py index e5dc9c48d..0429b2ad4 100644 --- a/fastvideo/models/mochi_hf/pipeline_mochi.py +++ b/fastvideo/models/mochi_hf/pipeline_mochi.py @@ -24,8 +24,7 @@ from diffusers.models.autoencoders import AutoencoderKL from diffusers.pipelines.mochi.pipeline_output import MochiPipelineOutput from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.schedulers import FlowMatchEulerDiscreteScheduler -from diffusers.utils import (is_torch_xla_available, logging, - replace_example_docstring) +from diffusers.utils import (is_torch_xla_available, logging, replace_example_docstring) from diffusers.utils.torch_utils import randn_tensor from diffusers.video_processor import VideoProcessor from einops import rearrange @@ -33,8 +32,7 @@ from transformers import T5EncoderModel, T5TokenizerFast from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel from fastvideo.utils.communications import all_gather -from fastvideo.utils.parallel_states import (get_sequence_parallel_state, - nccl_info) +from fastvideo.utils.parallel_states import (get_sequence_parallel_state, nccl_info) if is_torch_xla_available(): import torch_xla.core.xla_model as xm @@ -78,19 +76,14 @@ def calculate_shift( def linear_quadratic_schedule(num_steps, threshold_noise, linear_steps=None): if linear_steps is None: linear_steps = num_steps // 2 - linear_sigma_schedule = [ - i * threshold_noise / linear_steps for i in range(linear_steps) - ] + linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)] threshold_noise_step_diff = linear_steps - threshold_noise * num_steps quadratic_steps = num_steps - linear_steps - quadratic_coef = threshold_noise_step_diff / (linear_steps * - quadratic_steps**2) - linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / ( - quadratic_steps**2) + quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps**2) + linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps**2) const = quadratic_coef * (linear_steps**2) quadratic_sigma_schedule = [ - quadratic_coef * (i**2) + linear_coef * i + const - for i in range(linear_steps, num_steps) + quadratic_coef * (i**2) + linear_coef * i + const for i in range(linear_steps, num_steps) ] sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule sigma_schedule = [1.0 - x for x in sigma_schedule] @@ -130,28 +123,22 @@ def retrieve_timesteps( second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: - raise ValueError( - "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" - ) + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") if timesteps is not None: - accepts_timesteps = "timesteps" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys()) + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" timestep schedules. Please check whether you are using the correct scheduler." - ) + f" timestep schedules. Please check whether you are using the correct scheduler.") scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: - accept_sigmas = "sigmas" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys()) + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" sigmas schedules. Please check whether you are using the correct scheduler." - ) + f" sigmas schedules. Please check whether you are using the correct scheduler.") scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) @@ -187,9 +174,7 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): model_cpu_offload_seq = "text_encoder->transformer->vae" _optional_components = [] - _callback_tensor_inputs = [ - "latents", "prompt_embeds", "negative_prompt_embeds" - ] + _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] def __init__( self, @@ -212,11 +197,9 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): self.vae_temporal_scale_factor = 6 self.patch_size = 2 - self.video_processor = VideoProcessor( - vae_scale_factor=self.vae_spatial_scale_factor) + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_scale_factor) self.tokenizer_max_length = (self.tokenizer.model_max_length - if hasattr(self, "tokenizer") - and self.tokenizer is not None else 77) + if hasattr(self, "tokenizer") and self.tokenizer is not None else 77) self.default_height = 480 self.default_width = 848 @@ -247,31 +230,23 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): prompt_attention_mask = text_inputs.attention_mask prompt_attention_mask = prompt_attention_mask.bool().to(device) - untruncated_ids = self.tokenizer(prompt, - padding="longest", - return_tensors="pt").input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - if untruncated_ids.shape[-1] >= text_input_ids.shape[ - -1] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer.batch_decode( - untruncated_ids[:, max_sequence_length - 1:-1]) - logger.warning( - "The following part of your input was truncated because `max_sequence_length` is set to " - f" {max_sequence_length} tokens: {removed_text}") + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1:-1]) + logger.warning("The following part of your input was truncated because `max_sequence_length` is set to " + f" {max_sequence_length} tokens: {removed_text}") - prompt_embeds = self.text_encoder( - text_input_ids.to(device), attention_mask=prompt_attention_mask)[0] + prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) # duplicate text embeddings for each generation per prompt, using mps friendly method _, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) - prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, - seq_len, -1) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) prompt_attention_mask = prompt_attention_mask.view(batch_size, -1) - prompt_attention_mask = prompt_attention_mask.repeat( - num_videos_per_prompt, 1) + prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1) return prompt_embeds, prompt_attention_mask @@ -335,11 +310,9 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): if do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt or "" - negative_prompt = (batch_size * [negative_prompt] if isinstance( - negative_prompt, str) else negative_prompt) + negative_prompt = (batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt) - if prompt is not None and type(prompt) is not type( - negative_prompt): + if prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}.") @@ -379,13 +352,10 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): negative_prompt_attention_mask=None, ): if height % 8 != 0 or width % 8 != 0: - raise ValueError( - f"`height` and `width` have to be divisible by 8 but are {height} and {width}." - ) + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs - for k in callback_on_step_end_tensor_inputs): + if callback_on_step_end_tensor_inputs is not None and not all(k in self._callback_tensor_inputs + for k in callback_on_step_end_tensor_inputs): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) @@ -396,24 +366,15 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): " only forward one of the two.") elif prompt is None and prompt_embeds is None: raise ValueError( - "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." - ) - elif prompt is not None and (not isinstance(prompt, str) - and not isinstance(prompt, list)): - raise ValueError( - f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" - ) + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.") + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if prompt_embeds is not None and prompt_attention_mask is None: - raise ValueError( - "Must provide `prompt_attention_mask` when specifying `prompt_embeds`." - ) + raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.") - if (negative_prompt_embeds is not None - and negative_prompt_attention_mask is None): - raise ValueError( - "Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`." - ) + if (negative_prompt_embeds is not None and negative_prompt_attention_mask is None): + raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: @@ -479,13 +440,9 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) + f" size of {batch_size}. Make sure the batch size matches the length of the generators.") - latents = randn_tensor(shape, - generator=generator, - device=device, - dtype=torch.float32) + latents = randn_tensor(shape, generator=generator, device=device, dtype=torch.float32) latents = latents.to(dtype) return latents @@ -522,8 +479,7 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): timesteps: List[int] = None, guidance_scale: float = 4.5, num_videos_per_prompt: Optional[int] = 1, - generator: Optional[Union[torch.Generator, - List[torch.Generator]]] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, @@ -532,8 +488,7 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): output_type: Optional[str] = "pil", return_dict: bool = True, attention_kwargs: Optional[Dict[str, Any]] = None, - callback_on_step_end: Optional[Callable[[int, int, Dict], - None]] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], max_sequence_length: int = 256, return_all_states=False, @@ -612,8 +567,7 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): is returned where the first element is a list with the generated images. """ - if isinstance(callback_on_step_end, - (PipelineCallback, MultiPipelineCallbacks)): + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs height = height or self.default_height @@ -624,8 +578,7 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): prompt=prompt, height=height, width=width, - callback_on_step_end_tensor_inputs= - callback_on_step_end_tensor_inputs, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, prompt_attention_mask=prompt_attention_mask, @@ -665,10 +618,8 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): device=device, ) if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], - dim=0) - prompt_attention_mask = torch.cat( - [negative_prompt_attention_mask, prompt_attention_mask], dim=0) + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) # 4. Prepare latent variables num_channels_latents = self.transformer.config.in_channels @@ -685,17 +636,14 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): ) world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group if get_sequence_parallel_state(): - latents = rearrange(latents, - "b t (n s) h w -> b t n s h w", - n=world_size).contiguous() + latents = rearrange(latents, "b t (n s) h w -> b t n s h w", n=world_size).contiguous() latents = latents[:, :, rank, :, :, :] original_noise = copy.deepcopy(latents) # 5. Prepare timestep # from https://github.com/genmoai/models/blob/075b6e36db58f1242921deff83a1066887b9c9e1/src/mochi_preview/infer.py#L77 threshold_noise = 0.025 - sigmas = linear_quadratic_schedule(num_inference_steps, - threshold_noise) + sigmas = linear_quadratic_schedule(num_inference_steps, threshold_noise) sigmas = np.array(sigmas) # check if of type FlowMatchEulerDiscreteScheduler if isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler): @@ -712,25 +660,19 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): num_inference_steps, device, ) - num_warmup_steps = max( - len(timesteps) - num_inference_steps * self.scheduler.order, 0) + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) # 6. Denoising loop - self._progress_bar_config = { - "disable": nccl_info.rank_within_group != 0 - } + self._progress_bar_config = {"disable": nccl_info.rank_within_group != 0} with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue - latent_model_input = (torch.cat( - [latents] * - 2) if self.do_classifier_free_guidance else latents) + latent_model_input = (torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timestep = t.expand(latent_model_input.shape[0]).to( - latents.dtype) + timestep = t.expand(latent_model_input.shape[0]).to(latents.dtype) noise_pred = self.transformer( hidden_states=latent_model_input, @@ -745,15 +687,11 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): noise_pred = noise_pred.to(torch.float32) if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * ( - noise_pred_text - noise_pred_uncond) + noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype - latents = self.scheduler.step(noise_pred, - t, - latents.to(torch.float32), - return_dict=False)[0] + latents = self.scheduler.step(noise_pred, t, latents.to(torch.float32), return_dict=False)[0] latents = latents.to(latents_dtype) if latents.dtype != latents_dtype: @@ -765,17 +703,13 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end( - self, i, t, callback_kwargs) + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop( - "prompt_embeds", prompt_embeds) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) # call the callback, if provided - if i == len(timesteps) - 1 or ( - (i + 1) > num_warmup_steps and - (i + 1) % self.scheduler.order == 0): + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if XLA_AVAILABLE: @@ -795,25 +729,19 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin): else: # unscale/denormalize the latents # denormalize with the mean and std if available and not None - has_latents_mean = (hasattr(self.vae.config, "latents_mean") - and self.vae.config.latents_mean is not None) - has_latents_std = (hasattr(self.vae.config, "latents_std") - and self.vae.config.latents_std is not None) + has_latents_mean = (hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None) + has_latents_std = (hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None) if has_latents_mean and has_latents_std: - latents_mean = (torch.tensor( - self.vae.config.latents_mean).view(1, 12, 1, 1, 1).to( - latents.device, latents.dtype)) - latents_std = (torch.tensor(self.vae.config.latents_std).view( - 1, 12, 1, 1, 1).to(latents.device, latents.dtype)) - latents = ( - latents * latents_std / self.vae.config.scaling_factor + - latents_mean) + latents_mean = (torch.tensor(self.vae.config.latents_mean).view(1, 12, 1, 1, + 1).to(latents.device, latents.dtype)) + latents_std = (torch.tensor(self.vae.config.latents_std).view(1, 12, 1, 1, + 1).to(latents.device, latents.dtype)) + latents = (latents * latents_std / self.vae.config.scaling_factor + latents_mean) else: latents = latents / self.vae.config.scaling_factor video = self.vae.decode(latents, return_dict=False)[0] - video = self.video_processor.postprocess_video( - video, output_type=output_type) + video = self.video_processor.postprocess_video(video, output_type=output_type) # Offload all models self.maybe_free_model_hooks() diff --git a/fastvideo/models/stepvideo/__init__.py b/fastvideo/models/stepvideo/__init__.py new file mode 100644 index 000000000..e919871df --- /dev/null +++ b/fastvideo/models/stepvideo/__init__.py @@ -0,0 +1,7 @@ +import os + +os.environ["NCCL_DEBUG"] = "ERROR" + +from .diffusion.scheduler import * +from .diffusion.video_pipeline import * +from .modules.model import * \ No newline at end of file diff --git a/fastvideo/models/stepvideo/__version__.py b/fastvideo/models/stepvideo/__version__.py new file mode 100644 index 000000000..3f5c4a7d6 --- /dev/null +++ b/fastvideo/models/stepvideo/__version__.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/fastvideo/models/stepvideo/config.py b/fastvideo/models/stepvideo/config.py new file mode 100644 index 000000000..d7adb50bd --- /dev/null +++ b/fastvideo/models/stepvideo/config.py @@ -0,0 +1,174 @@ +import argparse + + +def parse_args(namespace=None): + parser = argparse.ArgumentParser(description="StepVideo inference script") + + parser = add_extra_models_args(parser) + parser = add_denoise_schedule_args(parser) + parser = add_inference_args(parser) + parser = add_parallel_args(parser) + + args = parser.parse_args(namespace=namespace) + + return args + + +def add_extra_models_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Extra models args, including vae, text encoders and tokenizers)") + + group.add_argument( + "--vae_url", + type=str, + default='127.0.0.1', + help="vae url.", + ) + group.add_argument( + "--caption_url", + type=str, + default='127.0.0.1', + help="caption url.", + ) + + return parser + + +def add_denoise_schedule_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Denoise schedule args") + + # Flow Matching + group.add_argument( + "--time_shift", + type=float, + default=7.0, + help="Shift factor for flow matching schedulers.", + ) + group.add_argument( + "--flow_reverse", + action="store_true", + help="If reverse, learning/sampling from t=1 -> t=0.", + ) + group.add_argument( + "--flow_solver", + type=str, + default="euler", + help="Solver for flow matching.", + ) + + return parser + + +def add_inference_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Inference args") + + # ======================== Model loads ======================== + group.add_argument( + "--model_dir", + type=str, + default="./ckpts", + help="Root path of all the models, including t2v models and extra models.", + ) + group.add_argument( + "--model_resolution", + type=str, + default="540p", + choices=["540p"], + help="Root path of all the models, including t2v models and extra models.", + ) + group.add_argument( + "--use-cpu-offload", + action="store_true", + help="Use CPU offload for the model load.", + ) + + # ======================== Inference general setting ======================== + group.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size for inference and evaluation.", + ) + group.add_argument( + "--infer_steps", + type=int, + default=50, + help="Number of denoising steps for inference.", + ) + group.add_argument( + "--save_path", + type=str, + default="./results", + help="Path to save the generated samples.", + ) + group.add_argument( + "--name_suffix", + type=str, + default="", + help="Suffix for the names of saved samples.", + ) + group.add_argument( + "--num_videos", + type=int, + default=1, + help="Number of videos to generate for each prompt.", + ) + # ---sample size--- + group.add_argument( + "--num_frames", + type=int, + default=204, + help="How many frames to sample from a video. ", + ) + group.add_argument( + "--height", + type=int, + default=544, + help="The height of video sample", + ) + group.add_argument( + "--width", + type=int, + default=992, + help="The width of video sample", + ) + # --- prompt --- + group.add_argument( + "--prompt", + type=str, + default=None, + help="Prompt for sampling during evaluation.", + ) + group.add_argument("--seed", type=int, default=1234, help="Seed for evaluation.") + + # Classifier-Free Guidance + group.add_argument("--pos_magic", + type=str, + default="超高清、HDR 视频、环境光、杜比全景声、画面稳定、流畅动作、逼真的细节、专业级构图、超现实主义、自然、生动、超细节、清晰。", + help="Positive magic prompt for sampling.") + group.add_argument("--neg_magic", + type=str, + default="画面暗、低分辨率、不良手、文本、缺少手指、多余的手指、裁剪、低质量、颗粒状、签名、水印、用户名、模糊。", + help="Negative magic prompt for sampling.") + group.add_argument("--cfg_scale", type=float, default=9.0, help="Classifier free guidance scale.") + + return parser + + +def add_parallel_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Parallel args") + + # ======================== Model loads ======================== + group.add_argument( + "--ulysses_degree", + type=int, + default=8, + help="Ulysses degree.", + ) + group.add_argument( + "--ring_degree", + type=int, + default=1, + help="Ulysses degree.", + ) + + return parser diff --git a/fastvideo/models/stepvideo/diffusion/scheduler.py b/fastvideo/models/stepvideo/diffusion/scheduler.py new file mode 100644 index 000000000..1246bbf97 --- /dev/null +++ b/fastvideo/models/stepvideo/diffusion/scheduler.py @@ -0,0 +1,220 @@ +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import SchedulerMixin +from diffusers.utils import BaseOutput, logging + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class FlowMatchDiscreteSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + """ + + prev_sample: torch.FloatTensor + + +class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin): + """ + Euler scheduler. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + timestep_spacing (`str`, defaults to `"linspace"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + reverse (`bool`, defaults to `True`): + Whether to reverse the timestep schedule. + """ + + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + reverse: bool = False, + solver: str = "euler", + device: Union[str, torch.device] = None, + ): + sigmas = torch.linspace(1, 0, num_train_timesteps + 1) + + if not reverse: + sigmas = sigmas.flip(0) + + self.sigmas = sigmas + # the value fed to model + self.timesteps = (sigmas[:-1] * num_train_timesteps).to(dtype=torch.float32) + + self._step_index = None + self._begin_index = None + + self.device = device + + self.supported_solver = ["euler"] + if solver not in self.supported_solver: + raise ValueError(f"Solver {solver} not supported. Supported solvers: {self.supported_solver}") + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def set_timesteps( + self, + num_inference_steps: int, + time_shift: float = 13.0, + device: Union[str, torch.device] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + n_tokens (`int`, *optional*): + Number of tokens in the input sequence. + """ + device = device or self.device + self.num_inference_steps = num_inference_steps + + sigmas = torch.linspace(1, 0, num_inference_steps + 1, device=device) + sigmas = self.sd3_time_shift(sigmas, time_shift) + + if not self.config.reverse: + sigmas = 1 - sigmas + + self.sigmas = sigmas + self.timesteps = sigmas[:-1] + + # Reset step index + self._step_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor: + return sample + + def sd3_time_shift(self, t: torch.Tensor, time_shift: float = 13.0): + return (time_shift * t) / (1 + (time_shift - 1) * t) + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + return_dict: bool = False, + ) -> Union[FlowMatchDiscreteSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + generator (`torch.Generator`, *optional*): + A random number generator. + n_tokens (`int`, *optional*): + Number of tokens in the input sequence. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or + tuple. + + Returns: + [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is + returned, otherwise a tuple is returned where the first element is the sample tensor. + """ + + if (isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor)): + raise ValueError(("Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep."), ) + + if self.step_index is None: + self._init_step_index(timestep) + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + + dt = self.sigmas[self.step_index + 1] - self.sigmas[self.step_index] + + if self.config.solver == "euler": + prev_sample = sample + model_output.to(torch.float32) * dt + else: + raise ValueError(f"Solver {self.config.solver} not supported. Supported solvers: {self.supported_solver}") + + # upon completion increase step index by one + self._step_index += 1 + + if not return_dict: + return prev_sample + + return FlowMatchDiscreteSchedulerOutput(prev_sample=prev_sample) + + def __len__(self): + return self.config.num_train_timesteps diff --git a/fastvideo/models/stepvideo/diffusion/video_pipeline.py b/fastvideo/models/stepvideo/diffusion/video_pipeline.py new file mode 100755 index 000000000..1293549d9 --- /dev/null +++ b/fastvideo/models/stepvideo/diffusion/video_pipeline.py @@ -0,0 +1,326 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. + +import asyncio +import pickle +from dataclasses import dataclass +from typing import Dict, List, Optional, Union + +import numpy as np +import torch +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.utils import BaseOutput + +from fastvideo.models.stepvideo.diffusion.scheduler import \ + FlowMatchDiscreteScheduler +from fastvideo.models.stepvideo.modules.model import StepVideoModel +from fastvideo.models.stepvideo.utils import VideoProcessor + + +def call_api_gen(url, api, port=8080): + url = f"http://{url}:{port}/{api}-api" + import aiohttp + + async def _fn(samples, *args, **kwargs): + if api == 'vae': + data = { + "samples": samples, + } + elif api == 'caption': + data = { + "prompts": samples, + } + else: + raise Exception(f"Not supported api: {api}...") + + async with aiohttp.ClientSession() as sess: + data_bytes = pickle.dumps(data) + async with sess.get(url, data=data_bytes, timeout=12000) as response: + result = bytearray() + while not response.content.at_eof(): + chunk = await response.content.read(1024) + result += chunk + response_data = pickle.loads(result) + return response_data + + return _fn + + +@dataclass +class StepVideoPipelineOutput(BaseOutput): + video: Union[torch.Tensor, np.ndarray] + + +class StepVideoPipeline(DiffusionPipeline): + r""" + Pipeline for text-to-video generation using StepVideo. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + Args: + transformer ([`StepVideoModel`]): + Conditional Transformer to denoise the encoded image latents. + scheduler ([`FlowMatchDiscreteScheduler`]): + A scheduler to be used in combination with `transformer` to denoise the encoded image latents. + vae_url: + remote vae server's url. + caption_url: + remote caption (stepllm and clip) server's url. + """ + + def __init__( + self, + transformer: StepVideoModel, + scheduler: FlowMatchDiscreteScheduler, + vae_url: str = '127.0.0.1', + caption_url: str = '127.0.0.1', + save_path: str = './results', + name_suffix: str = '', + ): + super().__init__() + + self.register_modules( + transformer=transformer, + scheduler=scheduler, + ) + + self.vae_scale_factor_temporal = self.vae.temporal_compression_ratio if getattr(self, "vae", None) else 8 + self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio if getattr(self, "vae", None) else 16 + self.video_processor = VideoProcessor(save_path, name_suffix) + + self.vae_url = vae_url + self.caption_url = caption_url + self.setup_api(self.vae_url, self.caption_url) + + def setup_api(self, vae_url, caption_url): + self.vae_url = vae_url + self.caption_url = caption_url + self.caption = call_api_gen(caption_url, 'caption') + self.vae = call_api_gen(vae_url, 'vae') + return self + + def encode_prompt( + self, + prompt: str, + neg_magic: str = '', + pos_magic: str = '', + ): + device = self._execution_device + prompts = [prompt + pos_magic] + bs = len(prompts) + prompts += [neg_magic] * bs + + data = asyncio.run(self.caption(prompts)) + prompt_embeds, prompt_attention_mask, clip_embedding = data['y'].to(device), data['y_mask'].to( + device), data['clip_embedding'].to(device) + + return prompt_embeds, clip_embedding, prompt_attention_mask + + def decode_vae(self, samples): + samples = asyncio.run(self.vae(samples.cpu())) + return samples + + def check_inputs(self, num_frames, width, height): + num_frames = max(num_frames // 17 * 17, 1) + width = max(width // 16 * 16, 16) + height = max(height // 16 * 16, 16) + return num_frames, width, height + + def prepare_latents( + self, + batch_size: int, + num_channels_latents: 64, + height: int = 544, + width: int = 992, + num_frames: int = 204, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + if latents is not None: + return latents.to(device=device, dtype=dtype) + + num_frames, width, height = self.check_inputs(num_frames, width, height) + shape = ( + batch_size, + max(num_frames // 17 * 3, 1), + num_channels_latents, + int(height) // self.vae_scale_factor_spatial, + int(width) // self.vae_scale_factor_spatial, + ) # b,f,c,h,w + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators.") + + if generator is None: + generator = torch.Generator(device=self._execution_device) + + latents = torch.randn(shape, generator=generator, device=device, dtype=dtype) + return latents + + @torch.inference_mode() + def __call__( + self, + prompt: Union[str, List[str]] = None, + height: int = 544, + width: int = 992, + num_frames: int = 204, + num_inference_steps: int = 50, + guidance_scale: float = 9.0, + time_shift: float = 13.0, + neg_magic: str = "", + pos_magic: str = "", + num_videos_per_prompt: Optional[int] = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + output_type: Optional[str] = "mp4", + output_file_name: Optional[str] = "", + return_dict: bool = True, + mask_strategy: Optional[Dict[str, list]] = None, + ): + r""" + The call function to the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + height (`int`, defaults to `544`): + The height in pixels of the generated image. + width (`int`, defaults to `992`): + The width in pixels of the generated image. + num_frames (`int`, defaults to `204`): + The number of frames in the generated video. + num_inference_steps (`int`, defaults to `50`): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, defaults to `9.0`): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.Tensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generated image. Choose between `PIL.Image` or `np.array`. + output_file_name(`str`, *optional*`): + The output mp4 file name. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`StepVideoPipelineOutput`] instead of a plain tuple. + + Examples: + + Returns: + [`~StepVideoPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`StepVideoPipelineOutput`] is returned, otherwise a `tuple` is returned + where the first element is a list with the generated images and the second element is a list of `bool`s + indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. + """ + + # 1. Check inputs. Raise error if not correct + device = self._execution_device + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + prompt_embeds, prompt_embeds_2, prompt_attention_mask = self.encode_prompt( + prompt=prompt, + neg_magic=neg_magic, + pos_magic=pos_magic, + ) + + transformer_dtype = self.transformer.dtype + prompt_embeds = prompt_embeds.to(transformer_dtype) + prompt_attention_mask = prompt_attention_mask.to(transformer_dtype) + prompt_embeds_2 = prompt_embeds_2.to(transformer_dtype) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps=num_inference_steps, time_shift=time_shift, device=device) + + # 5. Prepare latent variables + num_channels_latents = self.transformer.config.in_channels + latents = self.prepare_latents( + batch_size * num_videos_per_prompt, + num_channels_latents, + height, + width, + num_frames, + torch.bfloat16, + device, + generator, + latents, + ) + + def dict_to_3d_list(best_masks, t_max=50, l_max=48, h_max=48): + result = [[[None for _ in range(h_max)] for _ in range(l_max)] for _ in range(t_max)] + if best_masks is None: + return result + for key, value in best_masks.items(): + timestep, layer, head = map(int, key.split('_')) + result[timestep][layer][head] = value + return result + + mask_strategy = dict_to_3d_list(mask_strategy) + + #best_mask_selections = None + # 7. Denoising loop + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(self.scheduler.timesteps): + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = latent_model_input.to(transformer_dtype) + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latent_model_input.shape[0]).to(latent_model_input.dtype) + + noise_pred = self.transformer( + hidden_states=latent_model_input, + timestep=timestep, + encoder_hidden_states=prompt_embeds, + encoder_attention_mask=prompt_attention_mask, + encoder_hidden_states_2=prompt_embeds_2, + return_dict=False, + mask_strategy=mask_strategy[i], + ) + # perform guidance + if do_classifier_free_guidance: + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(model_output=noise_pred, timestep=t, sample=latents) + + progress_bar.update() + + if not torch.distributed.is_initialized() or int(torch.distributed.get_rank()) == 0: + if not output_type == "latent": + video = self.decode_vae(latents) + video = self.video_processor.postprocess_video(video, + output_file_name=output_file_name, + output_type=output_type) + else: + video = latents + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (video, ) + + return StepVideoPipelineOutput(video=video) diff --git a/fastvideo/models/stepvideo/modules/attentions.py b/fastvideo/models/stepvideo/modules/attentions.py new file mode 100755 index 000000000..79a37b980 --- /dev/null +++ b/fastvideo/models/stepvideo/modules/attentions.py @@ -0,0 +1,91 @@ +import torch +import torch.nn as nn +from einops import rearrange +from flash_attn import flash_attn_func +from st_attn import sliding_tile_attention + +from fastvideo.utils.communications import all_to_all_4D +from fastvideo.utils.parallel_states import (get_sequence_parallel_state, nccl_info) + + +class Attention(nn.Module): + + def __init__(self): + super().__init__() + + def attn_processor(self, attn_type): + if attn_type == 'torch': + return self.torch_attn_func + elif attn_type == 'parallel': + return self.parallel_attn_func + else: + raise Exception('Not supported attention type...') + + def tile(self, x, sp_size): + x = rearrange(x, "b (sp t h w) head d -> b (t sp h w) head d", sp=sp_size, t=36 // sp_size, h=48, w=48) + return rearrange(x, + "b (n_t ts_t n_h ts_h n_w ts_w) h d -> b (n_t n_h n_w ts_t ts_h ts_w) h d", + n_t=6, + n_h=6, + n_w=6, + ts_t=6, + ts_h=8, + ts_w=8) + + def untile(self, x, sp_size): + x = rearrange(x, + "b (n_t n_h n_w ts_t ts_h ts_w) h d -> b (n_t ts_t n_h ts_h n_w ts_w) h d", + n_t=6, + n_h=6, + n_w=6, + ts_t=6, + ts_h=8, + ts_w=8) + return rearrange(x, "b (t sp h w) head d -> b (sp t h w) head d", sp=sp_size, t=36 // sp_size, h=48, w=48) + + def torch_attn_func(self, q, k, v, attn_mask=None, causal=False, drop_rate=0.0, **kwargs): + + if attn_mask is not None and attn_mask.dtype != torch.bool: + attn_mask = attn_mask.to(q.dtype) + + if attn_mask is not None and attn_mask.ndim == 3: ## no head + n_heads = q.shape[2] + attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) + + q, k, v = map(lambda x: rearrange(x, 'b s h d -> b h s d'), (q, k, v)) + x = torch.nn.functional.scaled_dot_product_attention(q, + k, + v, + attn_mask=attn_mask, + dropout_p=drop_rate, + is_causal=causal) + x = rearrange(x, 'b h s d -> b s h d') + return x + + def parallel_attn_func(self, q, k, v, causal=False, mask_strategy=None, **kwargs): + if get_sequence_parallel_state(): + q = all_to_all_4D(q, scatter_dim=2, gather_dim=1) + k = all_to_all_4D(k, scatter_dim=2, gather_dim=1) + v = all_to_all_4D(v, scatter_dim=2, gather_dim=1) + + if mask_strategy[0] is not None: + q = self.tile(q, nccl_info.sp_size).transpose(1, 2).contiguous() + k = self.tile(k, nccl_info.sp_size).transpose(1, 2).contiguous() + v = self.tile(v, nccl_info.sp_size).transpose(1, 2).contiguous() + + head_num = q.size(1) # 48 // sp_size + current_rank = nccl_info.rank_within_group + + start_head = current_rank * head_num + windows = [mask_strategy[head_idx + start_head] for head_idx in range(head_num)] + + x = sliding_tile_attention(q, k, v, windows, 0, False).transpose(1, 2).contiguous() + x = self.untile(x, nccl_info.sp_size) + else: + x = flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False) + + if get_sequence_parallel_state(): + x = all_to_all_4D(x, scatter_dim=1, gather_dim=2) + + x = x.to(q.dtype) + return x diff --git a/fastvideo/models/stepvideo/modules/blocks.py b/fastvideo/models/stepvideo/modules/blocks.py new file mode 100755 index 000000000..befcfec7e --- /dev/null +++ b/fastvideo/models/stepvideo/modules/blocks.py @@ -0,0 +1,296 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +from typing import Optional + +import torch +import torch.nn as nn +from einops import rearrange + +from fastvideo.models.stepvideo.modules.attentions import Attention +from fastvideo.models.stepvideo.modules.normalization import RMSNorm +from fastvideo.models.stepvideo.modules.rope import RoPE3D + + +class SelfAttention(Attention): + + def __init__(self, hidden_dim, head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type='torch'): + super().__init__() + self.head_dim = head_dim + self.n_heads = hidden_dim // head_dim + + self.wqkv = nn.Linear(hidden_dim, hidden_dim * 3, bias=bias) + self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias) + + self.with_rope = with_rope + self.with_qk_norm = with_qk_norm + if self.with_qk_norm: + self.q_norm = RMSNorm(head_dim, elementwise_affine=True) + self.k_norm = RMSNorm(head_dim, elementwise_affine=True) + + if self.with_rope: + self.rope_3d = RoPE3D(freq=1e4, F0=1.0, scaling_factor=1.0) + self.rope_ch_split = [64, 32, 32] + + self.core_attention = self.attn_processor(attn_type=attn_type) + self.parallel = attn_type == 'parallel' + + def apply_rope3d(self, x, fhw_positions, rope_ch_split, parallel=True): + x = self.rope_3d(x, fhw_positions, rope_ch_split, parallel) + return x + + def forward(self, x, cu_seqlens=None, max_seqlen=None, rope_positions=None, attn_mask=None, mask_strategy=None): + xqkv = self.wqkv(x) + xqkv = xqkv.view(*x.shape[:-1], self.n_heads, 3 * self.head_dim) + + xq, xk, xv = torch.split(xqkv, [self.head_dim] * 3, dim=-1) ## seq_len, n, dim + + if self.with_qk_norm: + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + if self.with_rope: + xq = self.apply_rope3d(xq, rope_positions, self.rope_ch_split, parallel=self.parallel) + xk = self.apply_rope3d(xk, rope_positions, self.rope_ch_split, parallel=self.parallel) + + output = self.core_attention(xq, + xk, + xv, + cu_seqlens=cu_seqlens, + max_seqlen=max_seqlen, + attn_mask=attn_mask, + mask_strategy=mask_strategy) + output = rearrange(output, 'b s h d -> b s (h d)') + output = self.wo(output) + + return output + + +class CrossAttention(Attention): + + def __init__(self, hidden_dim, head_dim, bias=False, with_qk_norm=True, attn_type='torch'): + super().__init__() + self.head_dim = head_dim + self.n_heads = hidden_dim // head_dim + + self.wq = nn.Linear(hidden_dim, hidden_dim, bias=bias) + self.wkv = nn.Linear(hidden_dim, hidden_dim * 2, bias=bias) + self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias) + + self.with_qk_norm = with_qk_norm + if self.with_qk_norm: + self.q_norm = RMSNorm(head_dim, elementwise_affine=True) + self.k_norm = RMSNorm(head_dim, elementwise_affine=True) + + self.core_attention = self.attn_processor(attn_type=attn_type) + + def forward(self, x: torch.Tensor, encoder_hidden_states: torch.Tensor, attn_mask=None): + xq = self.wq(x) + xq = xq.view(*xq.shape[:-1], self.n_heads, self.head_dim) + + xkv = self.wkv(encoder_hidden_states) + xkv = xkv.view(*xkv.shape[:-1], self.n_heads, 2 * self.head_dim) + + xk, xv = torch.split(xkv, [self.head_dim] * 2, dim=-1) ## seq_len, n, dim + + if self.with_qk_norm: + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + output = self.core_attention(xq, xk, xv, attn_mask=attn_mask) + + output = rearrange(output, 'b s h d -> b s (h d)') + output = self.wo(output) + + return output + + +class GELU(nn.Module): + r""" + GELU activation function with tanh approximation support with `approximate="tanh"`. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation. + bias (`bool`, defaults to True): Whether to use a bias in the linear layer. + """ + + def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out, bias=bias) + self.approximate = approximate + + def gelu(self, gate: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.gelu(gate, approximate=self.approximate) + + def forward(self, hidden_states): + hidden_states = self.proj(hidden_states) + hidden_states = self.gelu(hidden_states) + return hidden_states + + +class FeedForward(nn.Module): + + def __init__( + self, + dim: int, + inner_dim: Optional[int] = None, + dim_out: Optional[int] = None, + mult: int = 4, + bias: bool = False, + ): + super().__init__() + inner_dim = dim * mult if inner_dim is None else inner_dim + dim_out = dim if dim_out is None else dim_out + self.net = nn.ModuleList([ + GELU(dim, inner_dim, approximate="tanh", bias=bias), + nn.Identity(), + nn.Linear(inner_dim, dim_out, bias=bias) + ]) + + def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor: + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +def modulate(x, scale, shift): + x = x * (1 + scale) + shift + return x + + +def gate(x, gate): + x = gate * x + return x + + +class StepVideoTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + upcast_attention (`bool`, *optional*): + Whether to upcast the attention computation to float32. This is useful for mixed precision training. + norm_elementwise_affine (`bool`, *optional*, defaults to `True`): + Whether to use learnable elementwise affine parameters for normalization. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. + final_dropout (`bool` *optional*, defaults to False): + Whether to apply a final dropout after the last feed-forward layer. + attention_type (`str`, *optional*, defaults to `"default"`): + The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. + positional_embeddings (`str`, *optional*, defaults to `None`): + The type of positional embeddings to apply to. + num_positional_embeddings (`int`, *optional*, defaults to `None`): + The maximum number of positional embeddings to apply. + """ + + def __init__(self, + dim: int, + attention_head_dim: int, + norm_eps: float = 1e-5, + ff_inner_dim: Optional[int] = None, + ff_bias: bool = False, + attention_type: str = 'parallel'): + super().__init__() + self.dim = dim + self.norm1 = nn.LayerNorm(dim, eps=norm_eps) + self.attn1 = SelfAttention(dim, + attention_head_dim, + bias=False, + with_rope=True, + with_qk_norm=True, + attn_type=attention_type) + + self.norm2 = nn.LayerNorm(dim, eps=norm_eps) + self.attn2 = CrossAttention(dim, attention_head_dim, bias=False, with_qk_norm=True, attn_type='torch') + + self.ff = FeedForward(dim=dim, inner_dim=ff_inner_dim, dim_out=dim, bias=ff_bias) + + self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5) + + @torch.no_grad() + def forward(self, + q: torch.Tensor, + kv: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + attn_mask=None, + rope_positions: list = None, + mask_strategy=None) -> torch.Tensor: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (torch.clone(chunk) for chunk in ( + self.scale_shift_table[None] + timestep.reshape(-1, 6, self.dim)).chunk(6, dim=1)) + + scale_shift_q = modulate(self.norm1(q), scale_msa, shift_msa) + + attn_q = self.attn1(scale_shift_q, rope_positions=rope_positions, mask_strategy=mask_strategy) + + q = gate(attn_q, gate_msa) + q + + attn_q = self.attn2(q, kv, attn_mask) + + q = attn_q + q + + scale_shift_q = modulate(self.norm2(q), scale_mlp, shift_mlp) + + ff_output = self.ff(scale_shift_q) + + q = gate(ff_output, gate_mlp) + q + + return q + + +class PatchEmbed(nn.Module): + """2D Image to Patch Embedding""" + + def __init__( + self, + patch_size=64, + in_channels=3, + embed_dim=768, + layer_norm=False, + flatten=True, + bias=True, + ): + super().__init__() + + self.flatten = flatten + self.layer_norm = layer_norm + + self.proj = nn.Conv2d(in_channels, + embed_dim, + kernel_size=(patch_size, patch_size), + stride=patch_size, + bias=bias) + + def forward(self, latent): + latent = self.proj(latent).to(latent.dtype) + if self.flatten: + latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC + if self.layer_norm: + latent = self.norm(latent) + + return latent diff --git a/fastvideo/models/stepvideo/modules/model.py b/fastvideo/models/stepvideo/modules/model.py new file mode 100755 index 000000000..e83b4dd1d --- /dev/null +++ b/fastvideo/models/stepvideo/modules/model.py @@ -0,0 +1,198 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +from typing import Dict, Optional + +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.modeling_utils import ModelMixin +from einops import rearrange, repeat +from torch import nn + +from fastvideo.models.stepvideo.modules.blocks import (PatchEmbed, StepVideoTransformerBlock) +from fastvideo.models.stepvideo.modules.normalization import (AdaLayerNormSingle, PixArtAlphaTextProjection) +from fastvideo.models.stepvideo.parallel import parallel_forward +from fastvideo.models.stepvideo.utils import with_empty_init + + +class StepVideoModel(ModelMixin, ConfigMixin): + _no_split_modules = ["StepVideoTransformerBlock", "PatchEmbed"] + + @with_empty_init + @register_to_config + def __init__( + self, + num_attention_heads: int = 48, + attention_head_dim: int = 128, + in_channels: int = 64, + out_channels: Optional[int] = 64, + num_layers: int = 48, + dropout: float = 0.0, + patch_size: int = 1, + norm_type: str = "ada_norm_single", + norm_elementwise_affine: bool = False, + norm_eps: float = 1e-6, + use_additional_conditions: Optional[bool] = False, + caption_channels: Optional[int] | list | tuple = [6144, 1024], + attention_type: Optional[str] = "parallel", + ): + super().__init__() + + # Set some common variables used across the board. + self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim + self.out_channels = in_channels if out_channels is None else out_channels + + self.use_additional_conditions = use_additional_conditions + + self.pos_embed = PatchEmbed( + patch_size=patch_size, + in_channels=self.config.in_channels, + embed_dim=self.inner_dim, + ) + + self.transformer_blocks = nn.ModuleList([ + StepVideoTransformerBlock(dim=self.inner_dim, + attention_head_dim=self.config.attention_head_dim, + attention_type=attention_type) for _ in range(self.config.num_layers) + ]) + + # 3. Output blocks. + self.norm_out = nn.LayerNorm(self.inner_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5) + self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels) + self.patch_size = patch_size + + self.adaln_single = AdaLayerNormSingle(self.inner_dim, use_additional_conditions=self.use_additional_conditions) + + if isinstance(self.config.caption_channels, int): + caption_channel = self.config.caption_channels + else: + caption_channel, clip_channel = self.config.caption_channels + self.clip_projection = nn.Linear(clip_channel, self.inner_dim) + + self.caption_norm = nn.LayerNorm(caption_channel, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + + self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channel, hidden_size=self.inner_dim) + + self.parallel = attention_type == 'parallel' + + def patchfy(self, hidden_states): + hidden_states = rearrange(hidden_states, 'b f c h w -> (b f) c h w') + hidden_states = self.pos_embed(hidden_states) + return hidden_states + + def prepare_attn_mask(self, encoder_attention_mask, encoder_hidden_states, q_seqlen): + kv_seqlens = encoder_attention_mask.sum(dim=1).int() + mask = torch.zeros([len(kv_seqlens), q_seqlen, max(kv_seqlens)], + dtype=torch.bool, + device=encoder_attention_mask.device) + encoder_hidden_states = encoder_hidden_states[:, :max(kv_seqlens)] + for i, kv_len in enumerate(kv_seqlens): + mask[i, :, :kv_len] = 1 + return encoder_hidden_states, mask + + @parallel_forward + def block_forward(self, + hidden_states, + encoder_hidden_states=None, + timestep=None, + rope_positions=None, + attn_mask=None, + parallel=True, + mask_strategy=None): + + for i, block in enumerate(self.transformer_blocks): + hidden_states = block(hidden_states, + encoder_hidden_states, + timestep=timestep, + attn_mask=attn_mask, + rope_positions=rope_positions, + mask_strategy=mask_strategy[i]) + + return hidden_states + + @torch.inference_mode() + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_hidden_states_2: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + added_cond_kwargs: Dict[str, torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + fps: torch.Tensor = None, + return_dict: bool = True, + mask_strategy=None, + ): + assert hidden_states.ndim == 5 + "hidden_states's shape should be (bsz, f, ch, h ,w)" + + bsz, frame, _, height, width = hidden_states.shape + height, width = height // self.patch_size, width // self.patch_size + + hidden_states = self.patchfy(hidden_states) + len_frame = hidden_states.shape[1] + + if self.use_additional_conditions: + added_cond_kwargs = { + "resolution": torch.tensor([(height, width)] * bsz, + device=hidden_states.device, + dtype=hidden_states.dtype), + "nframe": torch.tensor([frame] * bsz, device=hidden_states.device, dtype=hidden_states.dtype), + "fps": fps + } + else: + added_cond_kwargs = {} + + timestep, embedded_timestep = self.adaln_single(timestep, added_cond_kwargs=added_cond_kwargs) + + encoder_hidden_states = self.caption_projection(self.caption_norm(encoder_hidden_states)) + + if encoder_hidden_states_2 is not None and hasattr(self, 'clip_projection'): + clip_embedding = self.clip_projection(encoder_hidden_states_2) + encoder_hidden_states = torch.cat([clip_embedding, encoder_hidden_states], dim=1) + + hidden_states = rearrange(hidden_states, '(b f) l d-> b (f l) d', b=bsz, f=frame, l=len_frame).contiguous() + encoder_hidden_states, attn_mask = self.prepare_attn_mask(encoder_attention_mask, + encoder_hidden_states, + q_seqlen=frame * len_frame) + + hidden_states = self.block_forward(hidden_states, + encoder_hidden_states, + timestep=timestep, + rope_positions=[frame, height, width], + attn_mask=attn_mask, + parallel=self.parallel, + mask_strategy=mask_strategy) + + hidden_states = rearrange(hidden_states, 'b (f l) d -> (b f) l d', b=bsz, f=frame, l=len_frame) + + embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame).contiguous() + + shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1) + hidden_states = self.norm_out(hidden_states) + # Modulation + hidden_states = hidden_states * (1 + scale) + shift + hidden_states = self.proj_out(hidden_states) + + # unpatchify + hidden_states = hidden_states.reshape(shape=(-1, height, width, self.patch_size, self.patch_size, + self.out_channels)) + + hidden_states = rearrange(hidden_states, 'n h w p q c -> n c h p w q') + output = hidden_states.reshape(shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)) + + output = rearrange(output, '(b f) c h w -> b f c h w', f=frame) + + if return_dict: + return {'x': output} + return output diff --git a/fastvideo/models/stepvideo/modules/normalization.py b/fastvideo/models/stepvideo/modules/normalization.py new file mode 100755 index 000000000..18950be82 --- /dev/null +++ b/fastvideo/models/stepvideo/modules/normalization.py @@ -0,0 +1,312 @@ +import math +from typing import Dict, Optional, Tuple + +import torch +import torch.nn as nn + + +class RMSNorm(nn.Module): + + def __init__( + self, + dim: int, + elementwise_affine=True, + eps: float = 1e-6, + device=None, + dtype=None, + ): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.eps = eps + if elementwise_affine: + self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + if hasattr(self, "weight"): + output = output * self.weight + return output + + +ACTIVATION_FUNCTIONS = { + "swish": nn.SiLU(), + "silu": nn.SiLU(), + "mish": nn.Mish(), + "gelu": nn.GELU(), + "relu": nn.ReLU(), +} + + +def get_activation(act_fn: str) -> nn.Module: + """Helper function to get activation function from string. + + Args: + act_fn (str): Name of activation function. + + Returns: + nn.Module: Activation function. + """ + + act_fn = act_fn.lower() + if act_fn in ACTIVATION_FUNCTIONS: + return ACTIVATION_FUNCTIONS[act_fn] + else: + raise ValueError(f"Unsupported activation function: {act_fn}") + + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + flip_sin_to_cos: bool = False, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the + embeddings. :return: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + + # flip sine and cosine embeddings + if flip_sin_to_cos: + emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +class Timesteps(nn.Module): + + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + + def forward(self, timesteps): + t_emb = get_timestep_embedding( + timesteps, + self.num_channels, + flip_sin_to_cos=self.flip_sin_to_cos, + downscale_freq_shift=self.downscale_freq_shift, + ) + return t_emb + + +class TimestepEmbedding(nn.Module): + + def __init__(self, + in_channels: int, + time_embed_dim: int, + act_fn: str = "silu", + out_dim: int = None, + post_act_fn: Optional[str] = None, + cond_proj_dim=None, + sample_proj_bias=True): + super().__init__() + linear_cls = nn.Linear + + self.linear_1 = linear_cls( + in_channels, + time_embed_dim, + bias=sample_proj_bias, + ) + + if cond_proj_dim is not None: + self.cond_proj = linear_cls( + cond_proj_dim, + in_channels, + bias=False, + ) + else: + self.cond_proj = None + + self.act = get_activation(act_fn) + + if out_dim is not None: + time_embed_dim_out = out_dim + else: + time_embed_dim_out = time_embed_dim + + self.linear_2 = linear_cls( + time_embed_dim, + time_embed_dim_out, + bias=sample_proj_bias, + ) + + if post_act_fn is None: + self.post_act = None + else: + self.post_act = get_activation(post_act_fn) + + def forward(self, sample, condition=None): + if condition is not None: + sample = sample + self.cond_proj(condition) + sample = self.linear_1(sample) + + if self.act is not None: + sample = self.act(sample) + + sample = self.linear_2(sample) + + if self.post_act is not None: + sample = self.post_act(sample) + return sample + + +class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module): + + def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False): + super().__init__() + + self.outdim = size_emb_dim + self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) + + self.use_additional_conditions = use_additional_conditions + if self.use_additional_conditions: + self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) + self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim) + self.nframe_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) + self.fps_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) + + def forward(self, timestep, resolution=None, nframe=None, fps=None): + hidden_dtype = next(self.timestep_embedder.parameters()).dtype + + timesteps_proj = self.time_proj(timestep) + timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) + + if self.use_additional_conditions: + batch_size = timestep.shape[0] + resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype) + resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1) + nframe_emb = self.additional_condition_proj(nframe.flatten()).to(hidden_dtype) + nframe_emb = self.nframe_embedder(nframe_emb).reshape(batch_size, -1) + conditioning = timesteps_emb + resolution_emb + nframe_emb + + if fps is not None: + fps_emb = self.additional_condition_proj(fps.flatten()).to(hidden_dtype) + fps_emb = self.fps_embedder(fps_emb).reshape(batch_size, -1) + conditioning = conditioning + fps_emb + else: + conditioning = timesteps_emb + + return conditioning + + +class AdaLayerNormSingle(nn.Module): + r""" + Norm layer adaptive layer norm single (adaLN-single). + + As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3). + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + use_additional_conditions (`bool`): To use additional conditions for normalization or not. + """ + + def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, time_step_rescale=1000): + super().__init__() + + self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(embedding_dim, + size_emb_dim=embedding_dim // 2, + use_additional_conditions=use_additional_conditions) + + self.silu = nn.SiLU() + self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) + + self.time_step_rescale = time_step_rescale ## timestep usually in [0, 1], we rescale it to [0,1000] for stability + + def forward( + self, + timestep: torch.Tensor, + added_cond_kwargs: Dict[str, torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + embedded_timestep = self.emb(timestep * self.time_step_rescale, **added_cond_kwargs) + + out = self.linear(self.silu(embedded_timestep)) + + return out, embedded_timestep + + +class PixArtAlphaTextProjection(nn.Module): + """ + Projects caption embeddings. Also handles dropout for classifier-free guidance. + + Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py + """ + + def __init__(self, in_features, hidden_size): + super().__init__() + self.linear_1 = nn.Linear( + in_features, + hidden_size, + bias=True, + ) + self.act_1 = nn.GELU(approximate="tanh") + self.linear_2 = nn.Linear( + hidden_size, + hidden_size, + bias=True, + ) + + def forward(self, caption): + hidden_states = self.linear_1(caption) + hidden_states = self.act_1(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states diff --git a/fastvideo/models/stepvideo/modules/rope.py b/fastvideo/models/stepvideo/modules/rope.py new file mode 100755 index 000000000..c9b094d66 --- /dev/null +++ b/fastvideo/models/stepvideo/modules/rope.py @@ -0,0 +1,90 @@ +import torch + +from fastvideo.utils.parallel_states import nccl_info + + +class RoPE1D: + + def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0): + self.base = freq + self.F0 = F0 + self.scaling_factor = scaling_factor + self.cache = {} + + def get_cos_sin(self, D, seq_len, device, dtype): + if (D, seq_len, device, dtype) not in self.cache: + inv_freq = 1.0 / (self.base**(torch.arange(0, D, 2).float().to(device) / D)) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype) + freqs = torch.cat((freqs, freqs), dim=-1) + cos = freqs.cos() # (Seq, Dim) + sin = freqs.sin() + self.cache[D, seq_len, device, dtype] = (cos, sin) + return self.cache[D, seq_len, device, dtype] + + @staticmethod + def rotate_half(x): + x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:] + return torch.cat((-x2, x1), dim=-1) + + def apply_rope1d(self, tokens, pos1d, cos, sin): + assert pos1d.ndim == 2 + cos = torch.nn.functional.embedding(pos1d, cos)[:, :, None, :] + sin = torch.nn.functional.embedding(pos1d, sin)[:, :, None, :] + return (tokens * cos) + (self.rotate_half(tokens) * sin) + + def __call__(self, tokens, positions): + """ + input: + * tokens: batch_size x ntokens x nheads x dim + * positions: batch_size x ntokens (t position of each token) + output: + * tokens after applying RoPE2D (batch_size x ntokens x nheads x dim) + """ + D = tokens.size(3) + assert positions.ndim == 2 # Batch, Seq + cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype) + tokens = self.apply_rope1d(tokens, positions, cos, sin) + return tokens + + +class RoPE3D(RoPE1D): + + def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0): + super(RoPE3D, self).__init__(freq, F0, scaling_factor) + self.position_cache = {} + + def get_mesh_3d(self, rope_positions, bsz): + f, h, w = rope_positions + + if f"{f}-{h}-{w}" not in self.position_cache: + x = torch.arange(f, device='cpu') + y = torch.arange(h, device='cpu') + z = torch.arange(w, device='cpu') + self.position_cache[f"{f}-{h}-{w}"] = torch.cartesian_prod(x, y, z).view(1, f * h * w, 3).expand(bsz, -1, 3) + return self.position_cache[f"{f}-{h}-{w}"] + + def __call__(self, tokens, rope_positions, ch_split, parallel=False): + """ + input: + * tokens: batch_size x ntokens x nheads x dim + * rope_positions: list of (f, h, w) + output: + * tokens after applying RoPE2D (batch_size x ntokens x nheads x dim) + """ + assert sum(ch_split) == tokens.size(-1) + + mesh_grid = self.get_mesh_3d(rope_positions, bsz=tokens.shape[0]) + out = [] + for i, (D, x) in enumerate(zip(ch_split, torch.split(tokens, ch_split, dim=-1))): + cos, sin = self.get_cos_sin(D, int(mesh_grid.max()) + 1, tokens.device, tokens.dtype) + + if parallel: + mesh = torch.chunk(mesh_grid[:, :, i], nccl_info.sp_size, dim=1)[nccl_info.rank_within_group].clone() + else: + mesh = mesh_grid[:, :, i].clone() + x = self.apply_rope1d(x, mesh.to(tokens.device), cos, sin) + out.append(x) + + tokens = torch.cat(out, dim=-1) + return tokens diff --git a/fastvideo/models/stepvideo/parallel.py b/fastvideo/models/stepvideo/parallel.py new file mode 100644 index 000000000..9ce6ee3ce --- /dev/null +++ b/fastvideo/models/stepvideo/parallel.py @@ -0,0 +1,21 @@ +import torch + +from fastvideo.utils.communications import all_gather +from fastvideo.utils.parallel_states import nccl_info + + +def parallel_forward(fn_): + + def wrapTheFunction(_, hidden_states, *args, **kwargs): + if kwargs['parallel']: + hidden_states = torch.chunk(hidden_states, nccl_info.sp_size, dim=-2)[nccl_info.rank_within_group] + kwargs['attn_mask'] = torch.chunk(kwargs['attn_mask'], nccl_info.sp_size, + dim=-2)[nccl_info.rank_within_group] + output = fn_(_, hidden_states, *args, **kwargs) + + if kwargs['parallel']: + output = all_gather(output.contiguous(), dim=-2) + + return output + + return wrapTheFunction diff --git a/fastvideo/models/stepvideo/text_encoder/__init__.py b/fastvideo/models/stepvideo/text_encoder/__init__.py new file mode 100644 index 000000000..bb56aad46 --- /dev/null +++ b/fastvideo/models/stepvideo/text_encoder/__init__.py @@ -0,0 +1,12 @@ +import os + +import torch + +from fastvideo.models.stepvideo.config import parse_args + +try: + args = parse_args() + torch.ops.load_library( + os.path.join(args.model_dir, 'lib/liboptimus_ths-torch2.5-cu124.cpython-310-x86_64-linux-gnu.so')) +except Exception as err: + print(err) diff --git a/fastvideo/models/stepvideo/text_encoder/clip.py b/fastvideo/models/stepvideo/text_encoder/clip.py new file mode 100755 index 000000000..8e0386eef --- /dev/null +++ b/fastvideo/models/stepvideo/text_encoder/clip.py @@ -0,0 +1,36 @@ +import os + +import torch +import torch.nn as nn +from transformers import BertModel, BertTokenizer + + +class HunyuanClip(nn.Module): + """ + Hunyuan clip code copied from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py + hunyuan's clip used BertModel and BertTokenizer, so we copy it. + """ + + def __init__(self, model_dir, max_length=77): + super(HunyuanClip, self).__init__() + + self.max_length = max_length + self.tokenizer = BertTokenizer.from_pretrained(os.path.join(model_dir, 'tokenizer')) + self.text_encoder = BertModel.from_pretrained(os.path.join(model_dir, 'clip_text_encoder')) + + @torch.no_grad + def forward(self, prompts, with_mask=True): + self.device = next(self.text_encoder.parameters()).device + text_inputs = self.tokenizer( + prompts, + padding="max_length", + max_length=self.max_length, + truncation=True, + return_attention_mask=True, + return_tensors="pt", + ) + prompt_embeds = self.text_encoder( + text_inputs.input_ids.to(self.device), + attention_mask=text_inputs.attention_mask.to(self.device) if with_mask else None, + ) + return prompt_embeds.last_hidden_state, prompt_embeds.pooler_output diff --git a/fastvideo/models/stepvideo/text_encoder/flashattention.py b/fastvideo/models/stepvideo/text_encoder/flashattention.py new file mode 100755 index 000000000..647698f4b --- /dev/null +++ b/fastvideo/models/stepvideo/text_encoder/flashattention.py @@ -0,0 +1,45 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +import torch + + +def flash_attn_func(q, + k, + v, + dropout_p=0.0, + softmax_scale=None, + causal=True, + return_attn_probs=False, + tp_group_rank=0, + tp_group_size=1): + softmax_scale = q.size(-1)**(-0.5) if softmax_scale is None else softmax_scale + return torch.ops.Optimus.fwd(q, k, v, None, dropout_p, softmax_scale, causal, return_attn_probs, None, + tp_group_rank, tp_group_size)[0] + + +class FlashSelfAttention(torch.nn.Module): + + def __init__( + self, + attention_dropout=0.0, + ): + super().__init__() + self.dropout_p = attention_dropout + + def forward(self, q, k, v, cu_seqlens=None, max_seq_len=None): + if cu_seqlens is None: + output = flash_attn_func(q, k, v, dropout_p=self.dropout_p) + else: + raise ValueError('cu_seqlens is not supported!') + + return output diff --git a/fastvideo/models/stepvideo/text_encoder/stepllm.py b/fastvideo/models/stepvideo/text_encoder/stepllm.py new file mode 100755 index 000000000..1685db8da --- /dev/null +++ b/fastvideo/models/stepvideo/text_encoder/stepllm.py @@ -0,0 +1,292 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +import os +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from transformers.modeling_utils import PretrainedConfig, PreTrainedModel + +from fastvideo.models.stepvideo.modules.normalization import RMSNorm +from fastvideo.models.stepvideo.text_encoder.flashattention import \ + FlashSelfAttention +from fastvideo.models.stepvideo.text_encoder.tokenizer import (LLaMaEmbedding, Wrapped_StepChatTokenizer) +from fastvideo.models.stepvideo.utils import with_empty_init + + +def safediv(n, d): + q, r = divmod(n, d) + assert r == 0 + return q + + +class MultiQueryAttention(nn.Module): + + def __init__(self, cfg, layer_id=None): + super().__init__() + + self.head_dim = cfg.hidden_size // cfg.num_attention_heads + self.max_seq_len = cfg.seq_length + self.use_flash_attention = cfg.use_flash_attn + assert self.use_flash_attention, 'FlashAttention is required!' + + self.n_groups = cfg.num_attention_groups + self.tp_size = 1 + self.n_local_heads = cfg.num_attention_heads + self.n_local_groups = self.n_groups + + self.wqkv = nn.Linear( + cfg.hidden_size, + cfg.hidden_size + self.head_dim * 2 * self.n_groups, + bias=False, + ) + self.wo = nn.Linear( + cfg.hidden_size, + cfg.hidden_size, + bias=False, + ) + + assert self.use_flash_attention, 'non-Flash attention not supported yet.' + self.core_attention = FlashSelfAttention(attention_dropout=cfg.attention_dropout) + + self.layer_id = layer_id + + def forward( + self, + x: torch.Tensor, + mask: Optional[torch.Tensor], + cu_seqlens: Optional[torch.Tensor], + max_seq_len: Optional[torch.Tensor], + ): + seqlen, bsz, dim = x.shape + xqkv = self.wqkv(x) + + xq, xkv = torch.split( + xqkv, + (dim // self.tp_size, self.head_dim * 2 * self.n_groups // self.tp_size), + dim=-1, + ) + + # gather on 1st dimension + xq = xq.view(seqlen, bsz, self.n_local_heads, self.head_dim) + xkv = xkv.view(seqlen, bsz, self.n_local_groups, 2 * self.head_dim) + xk, xv = xkv.chunk(2, -1) + + # rotary embedding + flash attn + xq = rearrange(xq, "s b h d -> b s h d") + xk = rearrange(xk, "s b h d -> b s h d") + xv = rearrange(xv, "s b h d -> b s h d") + + q_per_kv = self.n_local_heads // self.n_local_groups + if q_per_kv > 1: + b, s, h, d = xk.size() + if h == 1: + xk = xk.expand(b, s, q_per_kv, d) + xv = xv.expand(b, s, q_per_kv, d) + else: + ''' To cover the cases where h > 1, we have + the following implementation, which is equivalent to: + xk = xk.repeat_interleave(q_per_kv, dim=-2) + xv = xv.repeat_interleave(q_per_kv, dim=-2) + but can avoid calling aten::item() that involves cpu. + ''' + idx = torch.arange(q_per_kv * h, device=xk.device).reshape(q_per_kv, -1).permute(1, 0).flatten() + xk = torch.index_select(xk.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous() + xv = torch.index_select(xv.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous() + + if self.use_flash_attention: + output = self.core_attention(xq, xk, xv, cu_seqlens=cu_seqlens, max_seq_len=max_seq_len) + # reduce-scatter only support first dimension now + output = rearrange(output, "b s h d -> s b (h d)").contiguous() + else: + xq, xk, xv = [rearrange(x, "b s ... -> s b ...").contiguous() for x in (xq, xk, xv)] + output = self.core_attention(xq, xk, xv, mask) + output = self.wo(output) + return output + + +class FeedForward(nn.Module): + + def __init__( + self, + cfg, + dim: int, + hidden_dim: int, + layer_id: int, + multiple_of: int = 256, + ): + super().__init__() + + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.swiglu = swiglu + + self.w1 = nn.Linear( + dim, + 2 * hidden_dim, + bias=False, + ) + self.w2 = nn.Linear( + hidden_dim, + dim, + bias=False, + ) + + def forward(self, x): + x = self.swiglu(self.w1(x)) + output = self.w2(x) + return output + + +class TransformerBlock(nn.Module): + + def __init__(self, cfg, layer_id: int): + super().__init__() + + self.n_heads = cfg.num_attention_heads + self.dim = cfg.hidden_size + self.head_dim = cfg.hidden_size // cfg.num_attention_heads + self.attention = MultiQueryAttention( + cfg, + layer_id=layer_id, + ) + + self.feed_forward = FeedForward( + cfg, + dim=cfg.hidden_size, + hidden_dim=cfg.ffn_hidden_size, + layer_id=layer_id, + ) + self.layer_id = layer_id + self.attention_norm = RMSNorm( + cfg.hidden_size, + eps=cfg.layernorm_epsilon, + ) + self.ffn_norm = RMSNorm( + cfg.hidden_size, + eps=cfg.layernorm_epsilon, + ) + + def forward( + self, + x: torch.Tensor, + mask: Optional[torch.Tensor], + cu_seqlens: Optional[torch.Tensor], + max_seq_len: Optional[torch.Tensor], + ): + residual = self.attention.forward(self.attention_norm(x), mask, cu_seqlens, max_seq_len) + h = x + residual + ffn_res = self.feed_forward.forward(self.ffn_norm(h)) + out = h + ffn_res + return out + + +class Transformer(nn.Module): + + def __init__( + self, + config, + max_seq_size=8192, + ): + super().__init__() + self.num_layers = config.num_layers + self.layers = self._build_layers(config) + + def _build_layers(self, config): + layers = torch.nn.ModuleList() + for layer_id in range(self.num_layers): + layers.append(TransformerBlock( + config, + layer_id=layer_id + 1, + )) + return layers + + def forward( + self, + hidden_states, + attention_mask, + cu_seqlens=None, + max_seq_len=None, + ): + + if max_seq_len is not None and not isinstance(max_seq_len, torch.Tensor): + max_seq_len = torch.tensor(max_seq_len, dtype=torch.int32, device="cpu") + + for lid, layer in enumerate(self.layers): + hidden_states = layer( + hidden_states, + attention_mask, + cu_seqlens, + max_seq_len, + ) + return hidden_states + + +class Step1Model(PreTrainedModel): + config_class = PretrainedConfig + + @with_empty_init + def __init__( + self, + config, + ): + super().__init__(config) + self.tok_embeddings = LLaMaEmbedding(config) + self.transformer = Transformer(config) + + def forward( + self, + input_ids=None, + attention_mask=None, + ): + + hidden_states = self.tok_embeddings(input_ids) + + hidden_states = self.transformer( + hidden_states, + attention_mask, + ) + return hidden_states + + +class STEP1TextEncoder(torch.nn.Module): + + def __init__(self, model_dir, max_length=320): + super(STEP1TextEncoder, self).__init__() + self.max_length = max_length + self.text_tokenizer = Wrapped_StepChatTokenizer(os.path.join(model_dir, 'step1_chat_tokenizer.model')) + text_encoder = Step1Model.from_pretrained(model_dir) + self.text_encoder = text_encoder.eval().to(torch.bfloat16) + + @torch.no_grad + def forward(self, prompts, with_mask=True, max_length=None): + self.device = next(self.text_encoder.parameters()).device + with torch.no_grad(), torch.cuda.amp.autocast(dtype=torch.bfloat16): + if type(prompts) is str: + prompts = [prompts] + + txt_tokens = self.text_tokenizer(prompts, + max_length=max_length or self.max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + y = self.text_encoder(txt_tokens.input_ids.to(self.device), + attention_mask=txt_tokens.attention_mask.to(self.device) if with_mask else None) + y_mask = txt_tokens.attention_mask + return y.transpose(0, 1), y_mask diff --git a/fastvideo/models/stepvideo/text_encoder/tokenizer.py b/fastvideo/models/stepvideo/text_encoder/tokenizer.py new file mode 100755 index 000000000..62ca0d608 --- /dev/null +++ b/fastvideo/models/stepvideo/text_encoder/tokenizer.py @@ -0,0 +1,209 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +from typing import List + +import torch +import torch.nn as nn + + +class LLaMaEmbedding(nn.Module): + """Language model embeddings. + + Arguments: + hidden_size: hidden size + vocab_size: vocabulary size + max_sequence_length: maximum size of sequence. This + is used for positional embedding + embedding_dropout_prob: dropout probability for embeddings + init_method: weight initialization method + num_tokentypes: size of the token-type embeddings. 0 value + will ignore this embedding + """ + + def __init__( + self, + cfg, + ): + super().__init__() + self.hidden_size = cfg.hidden_size + self.params_dtype = cfg.params_dtype + self.fp32_residual_connection = cfg.fp32_residual_connection + self.embedding_weights_in_fp32 = cfg.embedding_weights_in_fp32 + self.word_embeddings = torch.nn.Embedding( + cfg.padded_vocab_size, + self.hidden_size, + ) + self.embedding_dropout = torch.nn.Dropout(cfg.hidden_dropout) + + def forward(self, input_ids): + # Embeddings. + if self.embedding_weights_in_fp32: + self.word_embeddings = self.word_embeddings.to(torch.float32) + embeddings = self.word_embeddings(input_ids) + if self.embedding_weights_in_fp32: + embeddings = embeddings.to(self.params_dtype) + self.word_embeddings = self.word_embeddings.to(self.params_dtype) + + # Data format change to avoid explicit transposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + + # Dropout. + embeddings = self.embedding_dropout(embeddings) + + return embeddings + + +class StepChatTokenizer: + """Step Chat Tokenizer""" + + def __init__( + self, + model_file, + name="StepChatTokenizer", + bot_token="<|BOT|>", # Begin of Turn + eot_token="<|EOT|>", # End of Turn + call_start_token="<|CALL_START|>", # Call Start + call_end_token="<|CALL_END|>", # Call End + think_start_token="<|THINK_START|>", # Think Start + think_end_token="<|THINK_END|>", # Think End + mask_start_token="<|MASK_1e69f|>", # Mask start + mask_end_token="<|UNMASK_1e69f|>", # Mask end + ): + import sentencepiece + + self._tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file) + + self._vocab = {} + self._inv_vocab = {} + + self._special_tokens = {} + self._inv_special_tokens = {} + + self._t5_tokens = [] + + for idx in range(self._tokenizer.get_piece_size()): + text = self._tokenizer.id_to_piece(idx) + self._inv_vocab[idx] = text + self._vocab[text] = idx + + if self._tokenizer.is_control(idx) or self._tokenizer.is_unknown(idx): + self._special_tokens[text] = idx + self._inv_special_tokens[idx] = text + + self._unk_id = self._tokenizer.unk_id() + self._bos_id = self._tokenizer.bos_id() + self._eos_id = self._tokenizer.eos_id() + + for token in [bot_token, eot_token, call_start_token, call_end_token, think_start_token, think_end_token]: + assert token in self._vocab, f"Token '{token}' not found in tokenizer" + assert token in self._special_tokens, f"Token '{token}' is not a special token" + + for token in [mask_start_token, mask_end_token]: + assert token in self._vocab, f"Token '{token}' not found in tokenizer" + + self._bot_id = self._tokenizer.piece_to_id(bot_token) + self._eot_id = self._tokenizer.piece_to_id(eot_token) + self._call_start_id = self._tokenizer.piece_to_id(call_start_token) + self._call_end_id = self._tokenizer.piece_to_id(call_end_token) + self._think_start_id = self._tokenizer.piece_to_id(think_start_token) + self._think_end_id = self._tokenizer.piece_to_id(think_end_token) + self._mask_start_id = self._tokenizer.piece_to_id(mask_start_token) + self._mask_end_id = self._tokenizer.piece_to_id(mask_end_token) + + self._underline_id = self._tokenizer.piece_to_id("\u2581") + + @property + def vocab(self): + return self._vocab + + @property + def inv_vocab(self): + return self._inv_vocab + + @property + def vocab_size(self): + return self._tokenizer.vocab_size() + + def tokenize(self, text: str) -> List[int]: + return self._tokenizer.encode_as_ids(text) + + def detokenize(self, token_ids: List[int]) -> str: + return self._tokenizer.decode_ids(token_ids) + + +class Tokens: + + def __init__(self, input_ids, cu_input_ids, attention_mask, cu_seqlens, max_seq_len) -> None: + self.input_ids = input_ids + self.attention_mask = attention_mask + self.cu_input_ids = cu_input_ids + self.cu_seqlens = cu_seqlens + self.max_seq_len = max_seq_len + + def to(self, device): + self.input_ids = self.input_ids.to(device) + self.attention_mask = self.attention_mask.to(device) + self.cu_input_ids = self.cu_input_ids.to(device) + self.cu_seqlens = self.cu_seqlens.to(device) + return self + + +class Wrapped_StepChatTokenizer(StepChatTokenizer): + + def __call__(self, text, max_length=320, padding="max_length", truncation=True, return_tensors="pt"): + # [bos, ..., eos, pad, pad, ..., pad] + self.BOS = 1 + self.EOS = 2 + self.PAD = 2 + out_tokens = [] + attn_mask = [] + if len(text) == 0: + part_tokens = [self.BOS] + [self.EOS] + valid_size = len(part_tokens) + if len(part_tokens) < max_length: + part_tokens += [self.PAD] * (max_length - valid_size) + out_tokens.append(part_tokens) + attn_mask.append([1] * valid_size + [0] * (max_length - valid_size)) + else: + for part in text: + part_tokens = self.tokenize(part) + part_tokens = part_tokens[:(max_length - 2)] # leave 2 space for bos and eos + part_tokens = [self.BOS] + part_tokens + [self.EOS] + valid_size = len(part_tokens) + if len(part_tokens) < max_length: + part_tokens += [self.PAD] * (max_length - valid_size) + out_tokens.append(part_tokens) + attn_mask.append([1] * valid_size + [0] * (max_length - valid_size)) + + out_tokens = torch.tensor(out_tokens, dtype=torch.long) + attn_mask = torch.tensor(attn_mask, dtype=torch.long) + + # padding y based on tp size + padded_len = 0 + padded_flag = True if padded_len > 0 else False + if padded_flag: + pad_tokens = torch.tensor([[self.PAD] * max_length], device=out_tokens.device) + pad_attn_mask = torch.tensor([[1] * padded_len + [0] * (max_length - padded_len)], device=attn_mask.device) + out_tokens = torch.cat([out_tokens, pad_tokens], dim=0) + attn_mask = torch.cat([attn_mask, pad_attn_mask], dim=0) + + # cu_seqlens + cu_out_tokens = out_tokens.masked_select(attn_mask != 0).unsqueeze(0) + seqlen = attn_mask.sum(dim=1).tolist() + cu_seqlens = torch.cumsum(torch.tensor([0] + seqlen), 0).to(device=out_tokens.device, dtype=torch.int32) + max_seq_len = max(seqlen) + return Tokens(out_tokens, cu_out_tokens, attn_mask, cu_seqlens, max_seq_len) diff --git a/fastvideo/models/stepvideo/utils/__init__.py b/fastvideo/models/stepvideo/utils/__init__.py new file mode 100755 index 000000000..a77226b13 --- /dev/null +++ b/fastvideo/models/stepvideo/utils/__init__.py @@ -0,0 +1,2 @@ +from .utils import * +from .video_process import * \ No newline at end of file diff --git a/fastvideo/models/stepvideo/utils/quantization.py b/fastvideo/models/stepvideo/utils/quantization.py new file mode 100644 index 000000000..e87e38670 --- /dev/null +++ b/fastvideo/models/stepvideo/utils/quantization.py @@ -0,0 +1,117 @@ +# from stepvideo.diffusion.video_pipeline import StepVideoPipeline +import torch +import torch.nn as nn +from torch.nn import functional as F + + +def get_fp_maxval(bits=8, mantissa_bit=3, sign_bits=1): + _bits = torch.tensor(bits) + _mantissa_bit = torch.tensor(mantissa_bit) + _sign_bits = torch.tensor(sign_bits) + M = torch.clamp(torch.round(_mantissa_bit), 1, _bits - _sign_bits) + E = _bits - _sign_bits - M + bias = 2**(E - 1) - 1 + mantissa = 1 + for i in range(mantissa_bit - 1): + mantissa += 1 / (2**(i + 1)) + maxval = mantissa * 2**(2**E - 1 - bias) + return maxval + + +def quantize_to_fp8(x, bits=8, mantissa_bit=3, sign_bits=1): + """ + Default is E4M3. + """ + bits = torch.tensor(bits) + mantissa_bit = torch.tensor(mantissa_bit) + sign_bits = torch.tensor(sign_bits) + M = torch.clamp(torch.round(mantissa_bit), 1, bits - sign_bits) + E = bits - sign_bits - M + bias = 2**(E - 1) - 1 + mantissa = 1 + for i in range(mantissa_bit - 1): + mantissa += 1 / (2**(i + 1)) + maxval = mantissa * 2**(2**E - 1 - bias) + minval = -maxval + minval = -maxval if sign_bits == 1 else torch.zeros_like(maxval) + input_clamp = torch.min(torch.max(x, minval), maxval) + log_scales = torch.clamp((torch.floor(torch.log2(torch.abs(input_clamp)) + bias)).detach(), 1.0) + log_scales = 2.0**(log_scales - M - bias.type(x.dtype)) + # dequant + qdq_out = torch.round(input_clamp / log_scales) * log_scales + return qdq_out, log_scales + + +def fp8_tensor_quant(x, scale, bits=8, mantissa_bit=3, sign_bits=1): + for i in range(len(x.shape) - 1): + scale = scale.unsqueeze(-1) + new_x = x / scale + quant_dequant_x, log_scales = quantize_to_fp8(new_x, bits=bits, mantissa_bit=mantissa_bit, sign_bits=sign_bits) + return quant_dequant_x, scale, log_scales + + +def fp8_activation_dequant(qdq_out, scale, dtype): + qdq_out = qdq_out.type(dtype) + quant_dequant_x = qdq_out * scale.to(dtype) + return quant_dequant_x + + +def fp8_linear_forward(cls, original_dtype, input): + weight_dtype = cls.weight.dtype + ##### + if cls.weight.dtype != torch.float8_e4m3fn: + assert False + maxval = get_fp_maxval() + scale = torch.max(torch.abs(cls.weight.flatten())) / maxval + linear_weight, scale, log_scales = fp8_tensor_quant(cls.weight, scale) + linear_weight = linear_weight.to(torch.float8_e4m3fn) + weight_dtype = linear_weight.dtype + else: + scale = cls.fp8_scale.to(cls.weight.device) + linear_weight = cls.weight + ##### + + if weight_dtype == torch.float8_e4m3fn: + if True or len(input.shape) == 3: + cls_dequant = fp8_activation_dequant(linear_weight, scale, original_dtype) + if cls.bias is not None: + print(f"input dtype: {input.dtype}") + print(f"cls_dequant dtype: {cls_dequant.dtype}") + print(f"cls.bias dtype: {cls.bias.dtype}") + + output = F.linear(input, cls_dequant, cls.bias) + else: + output = F.linear(input, cls_dequant) + return output + else: + return cls.original_forward(input.to(original_dtype)) + else: + return cls.original_forward(input) + + +def convert_fp8_linear(module, original_dtype, params_to_keep={}): + setattr(module, "fp8_matmul_enabled", True) + fp8_layers = [] + scale_dict = {} + counter = 0 + for key, layer in module.named_modules(): + if isinstance(layer, nn.Linear) and 'transformer_blocks' in key: + print(f"Converting {key} to FP8") + fp8_layers.append(key) + original_forward = layer.forward + maxval = get_fp_maxval() + scale = torch.max(torch.abs(layer.weight.flatten())) / maxval + + original_weight = layer.weight.data # Store a reference to the original weights + quantized_weight, scale, _ = fp8_tensor_quant(original_weight, scale) + scale_dict[key] = scale + layer.weight = torch.nn.Parameter(quantized_weight.to(torch.float8_e4m3fn)) + del original_weight # Delete the reference to the original weights + torch.cuda.empty_cache() + + # print(f"layer weight dtype: {layer.weight.dtype} for layer {key}") + setattr(layer, "fp8_scale", scale.to(dtype=original_dtype)) + setattr(layer, "original_forward", original_forward) + setattr(layer, "forward", lambda input, m=layer: fp8_linear_forward(m, original_dtype, input)) + counter += 1 + return scale_dict diff --git a/fastvideo/models/stepvideo/utils/utils.py b/fastvideo/models/stepvideo/utils/utils.py new file mode 100755 index 000000000..835393277 --- /dev/null +++ b/fastvideo/models/stepvideo/utils/utils.py @@ -0,0 +1,60 @@ +import random +from functools import wraps + +import numpy as np +import torch +import torch.utils._device + + +def setup_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +class EmptyInitOnDevice(torch.overrides.TorchFunctionMode): + + def __init__(self, device=None): + self.device = device + + def __torch_function__(self, func, types, args=(), kwargs=None): + kwargs = kwargs or {} + if getattr(func, '__module__', None) == 'torch.nn.init': + if 'tensor' in kwargs: + return kwargs['tensor'] + else: + return args[0] + if self.device is not None and func in torch.utils._device._device_constructors( + ) and kwargs.get('device') is None: + kwargs['device'] = self.device + return func(*args, **kwargs) + + +def with_empty_init(func): + + @wraps(func) + def wrapper(*args, **kwargs): + with EmptyInitOnDevice('cpu'): + return func(*args, **kwargs) + + return wrapper + + +def culens2mask(cu_seqlens=None, cu_seqlens_kv=None, max_seqlen=None, max_seqlen_kv=None, is_causal=False): + assert len(cu_seqlens) == len(cu_seqlens_kv) + "q k v should have same bsz..." + bsz = len(cu_seqlens) - 1 + seqlens = cu_seqlens[1:] - cu_seqlens[:-1] + seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1] + + attn_mask = torch.zeros(bsz, max_seqlen, max_seqlen_kv, dtype=torch.bool) + for i, (seq_len, seq_len_kv) in enumerate(zip(seqlens, seqlens_kv)): + if is_causal: + attn_mask[i, :seq_len, :seq_len_kv] = torch.triu(torch.ones(seq_len, seq_len_kv), diagonal=1).bool() + else: + attn_mask[i, :seq_len, :seq_len_kv] = torch.ones([seq_len, seq_len_kv], dtype=torch.bool) + + return attn_mask diff --git a/fastvideo/models/stepvideo/utils/video_process.py b/fastvideo/models/stepvideo/utils/video_process.py new file mode 100644 index 000000000..93e6af546 --- /dev/null +++ b/fastvideo/models/stepvideo/utils/video_process.py @@ -0,0 +1,51 @@ +import os + +import imageio +import numpy as np +import torch + + +class VideoProcessor: + + def __init__(self, save_path: str = './results', name_suffix: str = ''): + self.save_path = save_path + os.makedirs(self.save_path, exist_ok=True) + self.name_suffix = name_suffix + + def crop2standard540p(self, vid_array): + _, height, width, _ = vid_array.shape + height_center = height // 2 + width_center = width // 2 + if width_center > height_center: ## horizon mode + return vid_array[:, height_center - 270:height_center + 270, width_center - 480:width_center + 480] + elif width_center < height_center: ## portrait mode + return vid_array[:, height_center - 480:height_center + 480, width_center - 270:width_center + 270] + else: + return vid_array + + def save_imageio_video(self, video_array: np.array, output_filename: str, fps=25, codec='libx264'): + + ffmpeg_params = [ + "-vf", + "atadenoise=0a=0.1:0b=0.1:1a=0.1:1b=0.1", # denoise + ] + + with imageio.get_writer(output_filename, fps=fps, codec=codec, ffmpeg_params=ffmpeg_params) as vid_writer: + for img_array in video_array: + vid_writer.append_data(img_array) + + def postprocess_video(self, video_tensor, output_file_name='', output_type="mp4", crop2standard540p=True): + if len(self.name_suffix) == 0: + video_path = os.path.join(self.save_path, f"{output_file_name}.{output_type}") + else: + video_path = os.path.join(self.save_path, f"{output_file_name}-{self.name_suffix}.{output_type}") + + video_tensor = torch.cat([t for t in video_tensor], dim=-2) + video_tensor = (video_tensor.cpu().clamp(-1, 1) + 1) * 127.5 + video_array = video_tensor.clamp(0, 255).to(torch.uint8).numpy().transpose(0, 2, 3, 1) + + if crop2standard540p: + video_array = self.crop2standard540p(video_array) + + self.save_imageio_video(video_array, video_path) + print(f"Saved the generated video in {video_path}") diff --git a/fastvideo/models/stepvideo/vae/vae.py b/fastvideo/models/stepvideo/vae/vae.py new file mode 100755 index 000000000..1dbf532c0 --- /dev/null +++ b/fastvideo/models/stepvideo/vae/vae.py @@ -0,0 +1,975 @@ +# Copyright 2025 StepFun Inc. All Rights Reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# ============================================================================== +import torch +from einops import rearrange +from torch import nn +from torch.nn import functional as F + +from fastvideo.models.stepvideo.utils import with_empty_init + + +def base_group_norm(x, norm_layer, act_silu=False, channel_last=False): + if hasattr(base_group_norm, 'spatial') and base_group_norm.spatial: + assert channel_last + x_shape = x.shape + x = x.flatten(0, 1) + if channel_last: + # Permute to NCHW format + x = x.permute(0, 3, 1, 2) + + out = F.group_norm(x.contiguous(), norm_layer.num_groups, norm_layer.weight, norm_layer.bias, norm_layer.eps) + if act_silu: + out = F.silu(out) + + if channel_last: + # Permute back to NHWC format + out = out.permute(0, 2, 3, 1) + + out = out.view(x_shape) + else: + if channel_last: + # Permute to NCHW format + x = x.permute(0, 3, 1, 2) + out = F.group_norm(x.contiguous(), norm_layer.num_groups, norm_layer.weight, norm_layer.bias, norm_layer.eps) + if act_silu: + out = F.silu(out) + if channel_last: + # Permute back to NHWC format + out = out.permute(0, 2, 3, 1) + return out + + +def base_conv2d(x, conv_layer, channel_last=False, residual=None): + if channel_last: + x = x.permute(0, 3, 1, 2) # NHWC to NCHW + out = F.conv2d(x, conv_layer.weight, conv_layer.bias, stride=conv_layer.stride, padding=conv_layer.padding) + if residual is not None: + if channel_last: + residual = residual.permute(0, 3, 1, 2) # NHWC to NCHW + out += residual + if channel_last: + out = out.permute(0, 2, 3, 1) # NCHW to NHWC + return out + + +def base_conv3d(x, conv_layer, channel_last=False, residual=None, only_return_output=False): + if only_return_output: + size = cal_outsize(x.shape, conv_layer.weight.shape, conv_layer.stride, conv_layer.padding) + return torch.empty(size, device=x.device, dtype=x.dtype) + if channel_last: + x = x.permute(0, 4, 1, 2, 3) # NDHWC to NCDHW + out = F.conv3d(x, conv_layer.weight, conv_layer.bias, stride=conv_layer.stride, padding=conv_layer.padding) + if residual is not None: + if channel_last: + residual = residual.permute(0, 4, 1, 2, 3) # NDHWC to NCDHW + out += residual + if channel_last: + out = out.permute(0, 2, 3, 4, 1) # NCDHW to NDHWC + return out + + +def cal_outsize(input_sizes, kernel_sizes, stride, padding): + stride_d, stride_h, stride_w = stride + padding_d, padding_h, padding_w = padding + dilation_d, dilation_h, dilation_w = 1, 1, 1 + + in_d = input_sizes[1] + in_h = input_sizes[2] + in_w = input_sizes[3] + + kernel_d = kernel_sizes[2] + kernel_h = kernel_sizes[3] + kernel_w = kernel_sizes[4] + out_channels = kernel_sizes[0] + + out_d = calc_out_(in_d, padding_d, dilation_d, kernel_d, stride_d) + out_h = calc_out_(in_h, padding_h, dilation_h, kernel_h, stride_h) + out_w = calc_out_(in_w, padding_w, dilation_w, kernel_w, stride_w) + size = [input_sizes[0], out_d, out_h, out_w, out_channels] + return size + + +def calc_out_(in_size, padding, dilation, kernel, stride): + return (in_size + 2 * padding - dilation * (kernel - 1) - 1) // stride + 1 + + +def base_conv3d_channel_last(x, conv_layer, residual=None): + in_numel = x.numel() + out_numel = int(x.numel() * conv_layer.out_channels / conv_layer.in_channels) + if (in_numel >= 2**30) or (out_numel >= 2**30): + assert conv_layer.stride[0] == 1, "time split asks time stride = 1" + + B, T, H, W, C = x.shape + K = conv_layer.kernel_size[0] + + chunks = 4 + chunk_size = T // chunks + + if residual is None: + out_nhwc = base_conv3d(x, conv_layer, channel_last=True, residual=residual, only_return_output=True) + else: + out_nhwc = residual + + assert B == 1 + for i in range(chunks): + if i == chunks - 1: + xi = x[:1, chunk_size * i:] + out_nhwci = out_nhwc[:1, chunk_size * i:] + else: + xi = x[:1, chunk_size * i:chunk_size * (i + 1) + K - 1] + out_nhwci = out_nhwc[:1, chunk_size * i:chunk_size * (i + 1)] + if residual is not None: + if i == chunks - 1: + ri = residual[:1, chunk_size * i:] + else: + ri = residual[:1, chunk_size * i:chunk_size * (i + 1)] + else: + ri = None + out_nhwci.copy_(base_conv3d(xi, conv_layer, channel_last=True, residual=ri)) + else: + out_nhwc = base_conv3d(x, conv_layer, channel_last=True, residual=residual) + return out_nhwc + + +class Upsample2D(nn.Module): + + def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + + if use_conv: + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1) + else: + assert "Not Supported" + self.conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1) + + def forward(self, x, output_size=None): + assert x.shape[-1] == self.channels + + if self.use_conv_transpose: + return self.conv(x) + + if output_size is None: + x = F.interpolate(x.permute(0, 3, 1, 2).to(memory_format=torch.channels_last), + scale_factor=2.0, + mode='nearest').permute(0, 2, 3, 1).contiguous() + else: + x = F.interpolate(x.permute(0, 3, 1, 2).to(memory_format=torch.channels_last), + size=output_size, + mode='nearest').permute(0, 2, 3, 1).contiguous() + + # x = self.conv(x) + x = base_conv2d(x, self.conv, channel_last=True) + return x + + +class Downsample2D(nn.Module): + + def __init__(self, channels, use_conv=False, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + + if use_conv: + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + assert self.channels == self.out_channels + self.conv = nn.AvgPool2d(kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[-1] == self.channels + if self.use_conv and self.padding == 0: + pad = (0, 0, 0, 1, 0, 1) + x = F.pad(x, pad, mode="constant", value=0) + + assert x.shape[-1] == self.channels + # x = self.conv(x) + x = base_conv2d(x, self.conv, channel_last=True) + return x + + +class CausalConv(nn.Module): + + def __init__(self, chan_in, chan_out, kernel_size, **kwargs): + super().__init__() + + if isinstance(kernel_size, int): + kernel_size = kernel_size if isinstance(kernel_size, tuple) else ((kernel_size, ) * 3) + time_kernel_size, height_kernel_size, width_kernel_size = kernel_size + + self.dilation = kwargs.pop('dilation', 1) + self.stride = kwargs.pop('stride', 1) + if isinstance(self.stride, int): + self.stride = (self.stride, 1, 1) + time_pad = self.dilation * (time_kernel_size - 1) + max((1 - self.stride[0]), 0) + height_pad = height_kernel_size // 2 + width_pad = width_kernel_size // 2 + self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0) + self.time_uncausal_padding = (width_pad, width_pad, height_pad, height_pad, 0, 0) + + self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, **kwargs) + self.is_first_run = True + + def forward(self, x, is_init=True, residual=None): + x = nn.functional.pad(x, self.time_causal_padding if is_init else self.time_uncausal_padding) + + x = self.conv(x) + if residual is not None: + x.add_(residual) + return x + + +class ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(nn.Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + factor: int, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.factor = factor + assert out_channels * factor**3 % in_channels == 0 + self.repeats = out_channels * factor**3 // in_channels + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + x = x.repeat_interleave(self.repeats, dim=1) + x = x.view(x.size(0), self.out_channels, self.factor, self.factor, self.factor, x.size(2), x.size(3), x.size(4)) + x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() + x = x.view(x.size(0), self.out_channels, + x.size(2) * self.factor, + x.size(4) * self.factor, + x.size(6) * self.factor) + x = x[:, :, self.factor - 1:, :, :] + return x + + +class ConvPixelShuffleUpSampleLayer3D(nn.Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + factor: int, + ): + super().__init__() + self.factor = factor + out_ratio = factor**3 + self.conv = CausalConv(in_channels, out_channels * out_ratio, kernel_size=kernel_size) + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + x = self.conv(x, is_init) + x = self.pixel_shuffle_3d(x, self.factor) + return x + + @staticmethod + def pixel_shuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor: + batch_size, channels, depth, height, width = x.size() + new_channels = channels // (factor**3) + new_depth = depth * factor + new_height = height * factor + new_width = width * factor + + x = x.view(batch_size, new_channels, factor, factor, factor, depth, height, width) + x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() + x = x.view(batch_size, new_channels, new_depth, new_height, new_width) + x = x[:, :, factor - 1:, :, :] + return x + + +class ConvPixelUnshuffleDownSampleLayer3D(nn.Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + factor: int, + ): + super().__init__() + self.factor = factor + out_ratio = factor**3 + assert out_channels % out_ratio == 0 + self.conv = CausalConv(in_channels, out_channels // out_ratio, kernel_size=kernel_size) + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + x = self.conv(x, is_init) + x = self.pixel_unshuffle_3d(x, self.factor) + return x + + @staticmethod + def pixel_unshuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor: + pad = (0, 0, 0, 0, factor - 1, 0) # (left, right, top, bottom, front, back) + x = F.pad(x, pad) + B, C, D, H, W = x.shape + x = x.view(B, C, D // factor, factor, H // factor, factor, W // factor, factor) + x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() + x = x.view(B, C * factor**3, D // factor, H // factor, W // factor) + return x + + +class PixelUnshuffleChannelAveragingDownSampleLayer3D(nn.Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + factor: int, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.factor = factor + assert in_channels * factor**3 % out_channels == 0 + self.group_size = in_channels * factor**3 // out_channels + + def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor: + pad = (0, 0, 0, 0, self.factor - 1, 0) # (left, right, top, bottom, front, back) + x = F.pad(x, pad) + B, C, D, H, W = x.shape + x = x.view(B, C, D // self.factor, self.factor, H // self.factor, self.factor, W // self.factor, self.factor) + x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() + x = x.view(B, C * self.factor**3, D // self.factor, H // self.factor, W // self.factor) + x = x.view(B, self.out_channels, self.group_size, D // self.factor, H // self.factor, W // self.factor) + x = x.mean(dim=2) + return x + + +def base_group_norm_with_zero_pad(x, norm_layer, act_silu=True, pad_size=2): + out_shape = list(x.shape) + out_shape[1] += pad_size + out = torch.empty(out_shape, dtype=x.dtype, device=x.device) + out[:, pad_size:] = base_group_norm(x, norm_layer, act_silu=act_silu, channel_last=True) + out[:, :pad_size] = 0 + return out + + +class CausalConvChannelLast(CausalConv): + + def __init__(self, chan_in, chan_out, kernel_size, **kwargs): + super().__init__(chan_in, chan_out, kernel_size, **kwargs) + + self.time_causal_padding = (0, 0) + self.time_causal_padding + self.time_uncausal_padding = (0, 0) + self.time_uncausal_padding + + def forward(self, x, is_init=True, residual=None): + if self.is_first_run: + self.is_first_run = False + # self.conv.weight = nn.Parameter(self.conv.weight.permute(0,2,3,4,1).contiguous()) + + x = nn.functional.pad(x, self.time_causal_padding if is_init else self.time_uncausal_padding) + + x = base_conv3d_channel_last(x, self.conv, residual=residual) + return x + + +class CausalConvAfterNorm(CausalConv): + + def __init__(self, chan_in, chan_out, kernel_size, **kwargs): + super().__init__(chan_in, chan_out, kernel_size, **kwargs) + + if self.time_causal_padding == (1, 1, 1, 1, 2, 0): + self.conv = nn.Conv3d(chan_in, + chan_out, + kernel_size, + stride=self.stride, + dilation=self.dilation, + padding=(0, 1, 1), + **kwargs) + else: + self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, **kwargs) + self.is_first_run = True + + def forward(self, x, is_init=True, residual=None): + if self.is_first_run: + self.is_first_run = False + + if self.time_causal_padding == (1, 1, 1, 1, 2, 0): + pass + else: + x = nn.functional.pad(x, self.time_causal_padding).contiguous() + + x = base_conv3d_channel_last(x, self.conv, residual=residual) + return x + + +class AttnBlock(nn.Module): + + def __init__(self, in_channels): + super().__init__() + + self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels) + self.q = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) + self.k = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) + self.v = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) + self.proj_out = CausalConvChannelLast(in_channels, in_channels, kernel_size=1) + + def attention(self, x, is_init=True): + x = base_group_norm(x, self.norm, act_silu=False, channel_last=True) + q = self.q(x, is_init) + k = self.k(x, is_init) + v = self.v(x, is_init) + + b, t, h, w, c = q.shape + q, k, v = map(lambda x: rearrange(x, "b t h w c -> b 1 (t h w) c"), (q, k, v)) + x = nn.functional.scaled_dot_product_attention(q, k, v, is_causal=True) + x = rearrange(x, "b 1 (t h w) c -> b t h w c", t=t, h=h, w=w) + + return x + + def forward(self, x): + x = x.permute(0, 2, 3, 4, 1).contiguous() + h = self.attention(x) + x = self.proj_out(h, residual=x) + x = x.permute(0, 4, 1, 2, 3) + return x + + +class Resnet3DBlock(nn.Module): + + def __init__( + self, + in_channels, + out_channels=None, + temb_channels=512, + conv_shortcut=False, + ): + super().__init__() + + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels) + self.conv1 = CausalConvAfterNorm(in_channels, out_channels, kernel_size=3) + if temb_channels > 0: + self.temb_proj = nn.Linear(temb_channels, out_channels) + + self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels) + self.conv2 = CausalConvAfterNorm(out_channels, out_channels, kernel_size=3) + + assert conv_shortcut is False + self.use_conv_shortcut = conv_shortcut + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = CausalConvAfterNorm(in_channels, out_channels, kernel_size=3) + else: + self.nin_shortcut = CausalConvAfterNorm(in_channels, out_channels, kernel_size=1) + + def forward(self, x, temb=None, is_init=True): + x = x.permute(0, 2, 3, 4, 1).contiguous() + + h = base_group_norm_with_zero_pad(x, self.norm1, act_silu=True, pad_size=2) + h = self.conv1(h) + if temb is not None: + h = h + self.temb_proj(nn.functional.silu(temb))[:, :, None, None] + + x = self.nin_shortcut(x) if self.in_channels != self.out_channels else x + + h = base_group_norm_with_zero_pad(h, self.norm2, act_silu=True, pad_size=2) + x = self.conv2(h, residual=x) + + x = x.permute(0, 4, 1, 2, 3) + return x + + +class Downsample3D(nn.Module): + + def __init__(self, in_channels, with_conv, stride): + super().__init__() + + self.with_conv = with_conv + if with_conv: + self.conv = CausalConv(in_channels, in_channels, kernel_size=3, stride=stride) + + def forward(self, x, is_init=True): + if self.with_conv: + x = self.conv(x, is_init) + else: + x = nn.functional.avg_pool3d(x, kernel_size=2, stride=2) + return x + + +class VideoEncoder(nn.Module): + + def __init__( + self, + ch=32, + ch_mult=(4, 8, 16, 16), + num_res_blocks=2, + in_channels=3, + z_channels=16, + double_z=True, + down_sampling_layer=[1, 2], + resamp_with_conv=True, + version=1, + ): + super().__init__() + + temb_ch = 0 + + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + + # downsampling + self.conv_in = CausalConv(in_channels, ch, kernel_size=3) + self.down_sampling_layer = down_sampling_layer + + in_ch_mult = (1, ) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(Resnet3DBlock(in_channels=block_in, out_channels=block_out, temb_channels=temb_ch)) + block_in = block_out + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + if i_level in self.down_sampling_layer: + down.downsample = Downsample3D(block_in, resamp_with_conv, stride=(2, 2, 2)) + else: + down.downsample = Downsample2D(block_in, resamp_with_conv, padding=0) #DIFF + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in) + self.version = version + if version == 2: + channels = 4 * z_channels * 2**3 + self.conv_patchify = ConvPixelUnshuffleDownSampleLayer3D(block_in, channels, kernel_size=3, factor=2) + self.shortcut_pathify = PixelUnshuffleChannelAveragingDownSampleLayer3D(block_in, channels, 2) + self.shortcut_out = PixelUnshuffleChannelAveragingDownSampleLayer3D( + channels, 2 * z_channels if double_z else z_channels, 1) + self.conv_out = CausalConvChannelLast(channels, 2 * z_channels if double_z else z_channels, kernel_size=3) + else: + self.conv_out = CausalConvAfterNorm(block_in, 2 * z_channels if double_z else z_channels, kernel_size=3) + + @torch.inference_mode() + def forward(self, x, video_frame_num, is_init=True): + # timestep embedding + temb = None + + t = video_frame_num + + # downsampling + h = self.conv_in(x, is_init) + + # make it real channel last, but behave like normal layout + h = h.permute(0, 2, 3, 4, 1).contiguous().permute(0, 4, 1, 2, 3) + + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb, is_init) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + + if i_level != self.num_resolutions - 1: + if isinstance(self.down[i_level].downsample, Downsample2D): + _, _, t, _, _ = h.shape + h = rearrange(h, "b c t h w -> (b t) h w c", t=t) + h = self.down[i_level].downsample(h) + h = rearrange(h, "(b t) h w c -> b c t h w", t=t) + else: + h = self.down[i_level].downsample(h, is_init) + + h = self.mid.block_1(h, temb, is_init) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb, is_init) + + h = h.permute(0, 2, 3, 4, 1).contiguous() # b c l h w -> b l h w c + if self.version == 2: + h = base_group_norm(h, self.norm_out, act_silu=True, channel_last=True) + h = h.permute(0, 4, 1, 2, 3).contiguous() + shortcut = self.shortcut_pathify(h, is_init) + h = self.conv_patchify(h, is_init) + h = h.add_(shortcut) + shortcut = self.shortcut_out(h, is_init).permute(0, 2, 3, 4, 1) + h = self.conv_out(h.permute(0, 2, 3, 4, 1).contiguous(), is_init) + h = h.add_(shortcut) + else: + h = base_group_norm_with_zero_pad(h, self.norm_out, act_silu=True, pad_size=2) + h = self.conv_out(h, is_init) + h = h.permute(0, 4, 1, 2, 3) # b l h w c -> b c l h w + + h = rearrange(h, "b c t h w -> b t c h w") + return h + + +class Res3DBlockUpsample(nn.Module): + + def __init__(self, input_filters, num_filters, down_sampling_stride, down_sampling=False): + super().__init__() + + self.input_filters = input_filters + self.num_filters = num_filters + + self.act_ = nn.SiLU(inplace=True) + + self.conv1 = CausalConvChannelLast(num_filters, num_filters, kernel_size=[3, 3, 3]) + self.norm1 = nn.GroupNorm(32, num_filters) + + self.conv2 = CausalConvChannelLast(num_filters, num_filters, kernel_size=[3, 3, 3]) + self.norm2 = nn.GroupNorm(32, num_filters) + + self.down_sampling = down_sampling + if down_sampling: + self.down_sampling_stride = down_sampling_stride + else: + self.down_sampling_stride = [1, 1, 1] + + if num_filters != input_filters or down_sampling: + self.conv3 = CausalConvChannelLast(input_filters, + num_filters, + kernel_size=[1, 1, 1], + stride=self.down_sampling_stride) + self.norm3 = nn.GroupNorm(32, num_filters) + + def forward(self, x, is_init=False): + x = x.permute(0, 2, 3, 4, 1).contiguous() + + residual = x + + h = self.conv1(x, is_init) + h = base_group_norm(h, self.norm1, act_silu=True, channel_last=True) + + h = self.conv2(h, is_init) + h = base_group_norm(h, self.norm2, act_silu=False, channel_last=True) + + if self.down_sampling or self.num_filters != self.input_filters: + x = self.conv3(x, is_init) + x = base_group_norm(x, self.norm3, act_silu=False, channel_last=True) + + h.add_(x) + h = self.act_(h) + if residual is not None: + h.add_(residual) + + h = h.permute(0, 4, 1, 2, 3) + return h + + +class Upsample3D(nn.Module): + + def __init__(self, in_channels, scale_factor=2): + super().__init__() + + self.scale_factor = scale_factor + self.conv3d = Res3DBlockUpsample(input_filters=in_channels, + num_filters=in_channels, + down_sampling_stride=(1, 1, 1), + down_sampling=False) + + def forward(self, x, is_init=True, is_split=True): + b, c, t, h, w = x.shape + + # x = x.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3).to(memory_format=torch.channels_last_3d) + if is_split: + split_size = c // 8 + x_slices = torch.split(x, split_size, dim=1) + x = [nn.functional.interpolate(x, scale_factor=self.scale_factor) for x in x_slices] + x = torch.cat(x, dim=1) + else: + x = nn.functional.interpolate(x, scale_factor=self.scale_factor) + + x = self.conv3d(x, is_init) + return x + + +class VideoDecoder(nn.Module): + + def __init__( + self, + ch=128, + z_channels=16, + out_channels=3, + ch_mult=(1, 2, 4, 4), + num_res_blocks=2, + temporal_up_layers=[2, 3], + temporal_downsample=4, + resamp_with_conv=True, + version=1, + ): + super().__init__() + + temb_ch = 0 + + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.temporal_downsample = temporal_downsample + + block_in = ch * ch_mult[self.num_resolutions - 1] + self.version = version + if version == 2: + channels = 4 * z_channels * 2**3 + self.conv_in = CausalConv(z_channels, channels, kernel_size=3) + self.shortcut_in = ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(z_channels, channels, 1) + self.conv_unpatchify = ConvPixelShuffleUpSampleLayer3D(channels, block_in, kernel_size=3, factor=2) + self.shortcut_unpathify = ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(channels, block_in, 2) + else: + self.conv_in = CausalConv(z_channels, block_in, kernel_size=3) + + # middle + self.mid = nn.Module() + self.mid.block_1 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch) + + # upsampling + self.up_id = len(temporal_up_layers) + self.video_frame_num = 1 + self.cur_video_frame_num = self.video_frame_num // 2**self.up_id + 1 + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append(Resnet3DBlock(in_channels=block_in, out_channels=block_out, temb_channels=temb_ch)) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + if i_level in temporal_up_layers: + up.upsample = Upsample3D(block_in) + self.cur_video_frame_num = self.cur_video_frame_num * 2 + else: + up.upsample = Upsample2D(block_in, resamp_with_conv) + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in) + self.conv_out = CausalConvAfterNorm(block_in, out_channels, kernel_size=3) + + @torch.inference_mode() + def forward(self, z, is_init=True): + z = rearrange(z, "b t c h w -> b c t h w") + + h = self.conv_in(z, is_init=is_init) + if self.version == 2: + shortcut = self.shortcut_in(z, is_init=is_init) + h = h.add_(shortcut) + shortcut = self.shortcut_unpathify(h, is_init=is_init) + h = self.conv_unpatchify(h, is_init=is_init) + h = h.add_(shortcut) + + temb = None + + h = h.permute(0, 2, 3, 4, 1).contiguous().permute(0, 4, 1, 2, 3) + h = self.mid.block_1(h, temb, is_init=is_init) + h = self.mid.attn_1(h) + h = h.permute(0, 2, 3, 4, 1).contiguous().permute(0, 4, 1, 2, 3) + h = self.mid.block_2(h, temb, is_init=is_init) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = h.permute(0, 2, 3, 4, 1).contiguous().permute(0, 4, 1, 2, 3) + h = self.up[i_level].block[i_block](h, temb, is_init=is_init) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + if isinstance(self.up[i_level].upsample, Upsample2D): + B = h.size(0) + h = h.permute(0, 2, 3, 4, 1).flatten(0, 1) + h = self.up[i_level].upsample(h) + h = h.unflatten(0, (B, -1)).permute(0, 4, 1, 2, 3) + else: + h = self.up[i_level].upsample(h, is_init=is_init) + + # end + h = h.permute(0, 2, 3, 4, 1) # b c l h w -> b l h w c + h = base_group_norm_with_zero_pad(h, self.norm_out, act_silu=True, pad_size=2) + h = self.conv_out(h) + h = h.permute(0, 4, 1, 2, 3) + + if is_init: + h = h[:, :, (self.temporal_downsample - 1):] + return h + + +def rms_norm(input, normalized_shape, eps=1e-6): + dtype = input.dtype + input = input.to(torch.float32) + variance = input.pow(2).flatten(-len(normalized_shape)).mean(-1)[(..., ) + (None, ) * len(normalized_shape)] + input = input * torch.rsqrt(variance + eps) + return input.to(dtype) + + +class DiagonalGaussianDistribution(object): + + def __init__(self, parameters, deterministic=False, rms_norm_mean=False, only_return_mean=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=-3) #N,[X],C,H,W + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + self.deterministic = deterministic + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean, + device=self.parameters.device, + dtype=self.parameters.dtype) + if rms_norm_mean: + self.mean = rms_norm(self.mean, self.mean.size()[1:]) + self.only_return_mean = only_return_mean + + def sample(self, generator=None): + # make sure sample is on the same device + # as the parameters and has same dtype + sample = torch.randn(self.mean.shape, generator=generator, device=self.parameters.device) + sample = sample.to(dtype=self.parameters.dtype) + x = self.mean + self.std * sample + if self.only_return_mean: + return self.mean + else: + return x + + +class AutoencoderKL(nn.Module): + + @with_empty_init + def __init__( + self, + in_channels=3, + out_channels=3, + z_channels=16, + num_res_blocks=2, + model_path=None, + weight_dict={}, + world_size=1, + version=1, + ): + super().__init__() + + self.frame_len = 17 + self.latent_len = 3 if version == 2 else 5 + + base_group_norm.spatial = True if version == 2 else False + + self.encoder = VideoEncoder( + in_channels=in_channels, + z_channels=z_channels, + num_res_blocks=num_res_blocks, + version=version, + ) + + self.decoder = VideoDecoder( + z_channels=z_channels, + out_channels=out_channels, + num_res_blocks=num_res_blocks, + version=version, + ) + + if model_path is not None: + weight_dict = self.init_from_ckpt(model_path) + if len(weight_dict) != 0: + self.load_from_dict(weight_dict) + self.convert_channel_last() + + self.world_size = world_size + + def init_from_ckpt(self, model_path): + from safetensors import safe_open + p = {} + with safe_open(model_path, framework="pt", device="cpu") as f: + for k in f.keys(): + tensor = f.get_tensor(k) + if k.startswith("decoder.conv_out."): + k = k.replace("decoder.conv_out.", "decoder.conv_out.conv.") + p[k] = tensor + return p + + def load_from_dict(self, p): + self.load_state_dict(p) + + def convert_channel_last(self): + #Conv2d NCHW->NHWC + pass + + def naive_encode(self, x, is_init_image=True): + b, len, c, h, w = x.size() + x = rearrange(x, 'b l c h w -> b c l h w').contiguous() + z = self.encoder(x, len, True) # 下采样[1, 4, 8, 16, 16] + return z + + @torch.inference_mode() + def encode(self, x): + # b (nc cf) c h w -> (b nc) cf c h w -> encode -> (b nc) cf c h w -> b (nc cf) c h w + chunks = list(x.split(self.frame_len, dim=1)) + for i in range(len(chunks)): + chunks[i] = self.naive_encode(chunks[i], True) + z = torch.cat(chunks, dim=1) + + posterior = DiagonalGaussianDistribution(z) + return posterior.sample() + + def decode_naive(self, z, is_init=True): + z = z.to(next(self.decoder.parameters()).dtype) + dec = self.decoder(z, is_init) + return dec + + @torch.inference_mode() + def decode(self, z): + # b (nc cf) c h w -> (b nc) cf c h w -> decode -> (b nc) c cf h w -> b (nc cf) c h w + chunks = list(z.split(self.latent_len, dim=1)) + + if self.world_size > 1: + chunks_total_num = len(chunks) + max_num_per_rank = (chunks_total_num + self.world_size - 1) // self.world_size + rank = torch.distributed.get_rank() + chunks_ = chunks[max_num_per_rank * rank:max_num_per_rank * (rank + 1)] + if len(chunks_) < max_num_per_rank: + chunks_.extend(chunks[:max_num_per_rank - len(chunks_)]) + chunks = chunks_ + + for i in range(len(chunks)): + chunks[i] = self.decode_naive(chunks[i], True).permute(0, 2, 1, 3, 4) + x = torch.cat(chunks, dim=1) + + if self.world_size > 1: + x_ = torch.empty([x.size(0), (self.world_size * max_num_per_rank) * self.frame_len, *x.shape[2:]], + dtype=x.dtype, + device=x.device) + torch.distributed.all_gather_into_tensor(x_, x) + x = x_[:, :chunks_total_num * self.frame_len] + + x = self.mix(x) + return x + + def mix(self, x): + remain_scale = 0.6 + mix_scale = 1. - remain_scale + front = slice(self.frame_len - 1, x.size(1) - 1, self.frame_len) + back = slice(self.frame_len, x.size(1), self.frame_len) + x[:, back] = x[:, back] * remain_scale + x[:, front] * mix_scale + x[:, front] = x[:, front] * remain_scale + x[:, back] * mix_scale + return x diff --git a/fastvideo/sample/call_remote_server_stepvideo.py b/fastvideo/sample/call_remote_server_stepvideo.py new file mode 100644 index 000000000..5f75218fe --- /dev/null +++ b/fastvideo/sample/call_remote_server_stepvideo.py @@ -0,0 +1,180 @@ +import argparse +import os +import pickle +import threading + +import torch +from flask import Blueprint, Flask, Response, request +from flask_restful import Api, Resource + +device = f'cuda:{torch.cuda.device_count()-1}' +dtype = torch.bfloat16 + + +def parsed_args(): + parser = argparse.ArgumentParser(description="StepVideo API Functions") + parser.add_argument('--model_dir', type=str) + parser.add_argument('--clip_dir', type=str, default='hunyuan_clip') + parser.add_argument('--llm_dir', type=str, default='step_llm') + parser.add_argument('--vae_dir', type=str, default='vae') + parser.add_argument('--port', type=str, default='8080') + args = parser.parse_args() + return args + + +class StepVaePipeline(Resource): + + def __init__(self, vae_dir, version=2): + self.vae = self.build_vae(vae_dir, version) + self.scale_factor = 1.0 + + def build_vae(self, vae_dir, version=2): + from fastvideo.models.stepvideo.vae.vae import AutoencoderKL + (model_name, z_channels) = ("vae_v2.safetensors", 64) if version == 2 else ("vae.safetensors", 16) + model_path = os.path.join(vae_dir, model_name) + + model = AutoencoderKL( + z_channels=z_channels, + model_path=model_path, + version=version, + ).to(dtype).to(device).eval() + print("Initialized vae...") + return model + + def decode(self, samples, *args, **kwargs): + with torch.no_grad(): + try: + dtype = next(self.vae.parameters()).dtype + device = next(self.vae.parameters()).device + samples = self.vae.decode(samples.to(dtype).to(device) / self.scale_factor) + if hasattr(samples, 'sample'): + samples = samples.sample + return samples + except: + torch.cuda.empty_cache() + return None + + +lock = threading.Lock() + + +class VAEapi(Resource): + + def __init__(self, vae_pipeline): + self.vae_pipeline = vae_pipeline + + def get(self): + with lock: + try: + feature = pickle.loads(request.get_data()) + feature['api'] = 'vae' + + feature = {k: v for k, v in feature.items() if v is not None} + video_latents = self.vae_pipeline.decode(**feature) + response = pickle.dumps(video_latents) + + except Exception as e: + print("Caught Exception: ", e) + return Response(e) + + return Response(response) + + +class CaptionPipeline(Resource): + + def __init__(self, llm_dir, clip_dir): + self.text_encoder = self.build_llm(llm_dir) + self.clip = self.build_clip(clip_dir) + + def build_llm(self, model_dir): + from fastvideo.models.stepvideo.text_encoder.stepllm import \ + STEP1TextEncoder + text_encoder = STEP1TextEncoder(model_dir, max_length=320).to(dtype).to(device).eval() + print("Initialized text encoder...") + return text_encoder + + def build_clip(self, model_dir): + from fastvideo.models.stepvideo.text_encoder.clip import HunyuanClip + clip = HunyuanClip(model_dir, max_length=77).to(device).eval() + print("Initialized clip encoder...") + return clip + + def embedding(self, prompts, *args, **kwargs): + with torch.no_grad(): + try: + y, y_mask = self.text_encoder(prompts) + + clip_embedding, _ = self.clip(prompts) + + len_clip = clip_embedding.shape[1] + y_mask = torch.nn.functional.pad(y_mask, (len_clip, 0), + value=1) ## pad attention_mask with clip's length + + data = { + 'y': y.detach().cpu(), + 'y_mask': y_mask.detach().cpu(), + 'clip_embedding': clip_embedding.to(torch.bfloat16).detach().cpu() + } + + return data + except Exception as err: + print(f"{err}") + return None + + +lock = threading.Lock() + + +class Captionapi(Resource): + + def __init__(self, caption_pipeline): + self.caption_pipeline = caption_pipeline + + def get(self): + with lock: + try: + feature = pickle.loads(request.get_data()) + feature['api'] = 'caption' + + feature = {k: v for k, v in feature.items() if v is not None} + embeddings = self.caption_pipeline.embedding(**feature) + response = pickle.dumps(embeddings) + + except Exception as e: + print("Caught Exception: ", e) + return Response(e) + + return Response(response) + + +class RemoteServer(object): + + def __init__(self, args) -> None: + self.app = Flask(__name__) + root = Blueprint("root", __name__) + self.app.register_blueprint(root) + api = Api(self.app) + + self.vae_pipeline = StepVaePipeline(vae_dir=os.path.join(args.model_dir, args.vae_dir)) + api.add_resource( + VAEapi, + "/vae-api", + resource_class_args=[self.vae_pipeline], + ) + + self.caption_pipeline = CaptionPipeline(llm_dir=os.path.join(args.model_dir, args.llm_dir), + clip_dir=os.path.join(args.model_dir, args.clip_dir)) + api.add_resource( + Captionapi, + "/caption-api", + resource_class_args=[self.caption_pipeline], + ) + + def run(self, host="0.0.0.0", port=8080): + self.app.run(host, port=port, threaded=True, debug=False) + + +if __name__ == "__main__": + args = parsed_args() + flask_server = RemoteServer(args) + flask_server.run(host="0.0.0.0", port=args.port) diff --git a/fastvideo/sample/generate_synthetic.py b/fastvideo/sample/generate_synthetic.py index 42550db9b..9fda825f9 100644 --- a/fastvideo/sample/generate_synthetic.py +++ b/fastvideo/sample/generate_synthetic.py @@ -9,8 +9,7 @@ from diffusers.utils import export_to_video from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline -def generate_video_and_latent(pipe, prompt, height, width, num_frames, - num_inference_steps, guidance_scale): +def generate_video_and_latent(pipe, prompt, height, width, num_frames, num_inference_steps, guidance_scale): # Set the random seed for reproducibility generator = torch.Generator("cuda").manual_seed(12345) # Generate videos from the input prompt @@ -25,8 +24,7 @@ def generate_video_and_latent(pipe, prompt, height, width, num_frames, output_type="latent_and_video", ) # prompt_embed has negative prompt at index 0 - return noise[0], video[0], latent[0], prompt_embed[ - 1], prompt_attention_mask[1] + return noise[0], video[0], latent[0], prompt_embed[1], prompt_attention_mask[1] # return dummy tensor to debug first # return torch.zeros(1, 3, 480, 848), torch.zeros(1, 256, 16, 16) @@ -40,22 +38,15 @@ if __name__ == "__main__": parser.add_argument("--num_inference_steps", type=int, default=64) parser.add_argument("--guidance_scale", type=float, default=4.5) parser.add_argument("--model_path", type=str, default="data/mochi") - parser.add_argument("--prompt_path", - type=str, - default="data/dummyVid/videos2caption.json") - parser.add_argument("--dataset_output_dir", - type=str, - default="data/dummySynthetic") + parser.add_argument("--prompt_path", type=str, default="data/dummyVid/videos2caption.json") + parser.add_argument("--dataset_output_dir", type=str, default="data/dummySynthetic") args = parser.parse_args() local_rank = int(os.getenv("RANK", 0)) world_size = int(os.getenv("WORLD_SIZE", 1)) print("world_size", world_size, "local rank", local_rank) torch.cuda.set_device(local_rank) - dist.init_process_group(backend="nccl", - init_method="env://", - world_size=world_size, - rank=local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) if not isinstance(args.prompt_path, list): args.prompt_path = [args.prompt_path] @@ -63,8 +54,7 @@ if __name__ == "__main__": text_prompt = open(args.prompt_path[0], "r").readlines() text_prompt = [i.strip() for i in text_prompt] - pipe = MochiPipeline.from_pretrained(args.model_path, - torch_dtype=torch.bfloat16) + pipe = MochiPipeline.from_pretrained(args.model_path, torch_dtype=torch.bfloat16) pipe.enable_vae_tiling() pipe.enable_model_cpu_offload(gpu_id=local_rank) # make dir if not exist @@ -73,10 +63,8 @@ if __name__ == "__main__": os.makedirs(os.path.join(args.dataset_output_dir, "noise"), exist_ok=True) os.makedirs(os.path.join(args.dataset_output_dir, "video"), exist_ok=True) os.makedirs(os.path.join(args.dataset_output_dir, "latent"), exist_ok=True) - os.makedirs(os.path.join(args.dataset_output_dir, "prompt_embed"), - exist_ok=True) - os.makedirs(os.path.join(args.dataset_output_dir, "prompt_attention_mask"), - exist_ok=True) + os.makedirs(os.path.join(args.dataset_output_dir, "prompt_embed"), exist_ok=True) + os.makedirs(os.path.join(args.dataset_output_dir, "prompt_attention_mask"), exist_ok=True) data = [] for i, prompt in enumerate(text_prompt): if i % world_size != local_rank: @@ -98,17 +86,11 @@ if __name__ == "__main__": ) # save latent video_name = str(i) - noise_path = os.path.join(args.dataset_output_dir, "noise", - video_name + ".pt") - latent_path = os.path.join(args.dataset_output_dir, "latent", - video_name + ".pt") - prompt_embed_path = os.path.join(args.dataset_output_dir, - "prompt_embed", video_name + ".pt") - video_path = os.path.join(args.dataset_output_dir, "video", - video_name + ".mp4") - prompt_attention_mask_path = os.path.join(args.dataset_output_dir, - "prompt_attention_mask", - video_name + ".pt") + noise_path = os.path.join(args.dataset_output_dir, "noise", video_name + ".pt") + latent_path = os.path.join(args.dataset_output_dir, "latent", video_name + ".pt") + prompt_embed_path = os.path.join(args.dataset_output_dir, "prompt_embed", video_name + ".pt") + video_path = os.path.join(args.dataset_output_dir, "video", video_name + ".mp4") + prompt_attention_mask_path = os.path.join(args.dataset_output_dir, "prompt_attention_mask", video_name + ".pt") # save latent torch.save(noise, noise_path) torch.save(latent, latent_path) @@ -132,6 +114,5 @@ if __name__ == "__main__": # save json if local_rank == 0: all_data = [item for sublist in gathered_data for item in sublist] - with open(os.path.join(args.dataset_output_dir, "videos2caption.json"), - "w") as f: + with open(os.path.join(args.dataset_output_dir, "videos2caption.json"), "w") as f: json.dump(all_data, f, indent=4) diff --git a/fastvideo/sample/sample_t2v_hunyuan.py b/fastvideo/sample/sample_t2v_hunyuan.py index f4b6e3960..f33f2c731 100644 --- a/fastvideo/sample/sample_t2v_hunyuan.py +++ b/fastvideo/sample/sample_t2v_hunyuan.py @@ -10,8 +10,7 @@ import torchvision from einops import rearrange from fastvideo.models.hunyuan.inference import HunyuanVideoSampler -from fastvideo.utils.parallel_states import ( - initialize_sequence_parallel_state, nccl_info) +from fastvideo.utils.parallel_states import (initialize_sequence_parallel_state, nccl_info) def initialize_distributed(): @@ -19,10 +18,7 @@ def initialize_distributed(): world_size = int(os.getenv("WORLD_SIZE", 1)) print("world_size", world_size) torch.cuda.set_device(local_rank) - dist.init_process_group(backend="nccl", - init_method="env://", - world_size=world_size, - rank=local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) initialize_sequence_parallel_state(world_size) @@ -40,14 +36,16 @@ def main(args): os.makedirs(os.path.dirname(save_path), exist_ok=True) # Load models - hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained( - models_root_path, args=args) + hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(models_root_path, args=args) # Get the updated args args = hunyuan_video_sampler.args - with open(args.prompt) as f: - prompts = f.readlines() + if args.prompt.endswith('.txt'): + with open(args.prompt) as f: + prompts = [line.strip() for line in f.readlines()] + else: + prompts = [args.prompt] for prompt in prompts: outputs = hunyuan_video_sampler.predict( @@ -71,9 +69,7 @@ def main(args): x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) outputs.append((x * 255).numpy().astype(np.uint8)) os.makedirs(os.path.dirname(args.output_path), exist_ok=True) - imageio.mimsave(os.path.join(args.output_path, f"{prompt[:100]}.mp4"), - outputs, - fps=args.fps) + imageio.mimsave(os.path.join(args.output_path, f"{prompt[:100]}.mp4"), outputs, fps=args.fps) if __name__ == "__main__": @@ -96,14 +92,8 @@ if __name__ == "__main__": default="flow", help="Denoise type for noised inputs.", ) - parser.add_argument("--seed", - type=int, - default=None, - help="Seed for evaluation.") - parser.add_argument("--neg_prompt", - type=str, - default=None, - help="Negative prompt for sampling.") + parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.") + parser.add_argument("--neg_prompt", type=str, default=None, help="Negative prompt for sampling.") parser.add_argument( "--guidance_scale", type=float, @@ -116,14 +106,8 @@ if __name__ == "__main__": default=6.0, help="Embedded classifier free guidance scale.", ) - parser.add_argument("--flow_shift", - type=int, - default=7, - help="Flow shift parameter.") - parser.add_argument("--batch_size", - type=int, - default=1, - help="Batch size for inference.") + parser.add_argument("--flow_shift", type=int, default=7, help="Flow shift parameter.") + parser.add_argument("--batch_size", type=int, default=1, help="Batch size for inference.") parser.add_argument( "--num_videos", type=int, @@ -134,8 +118,7 @@ if __name__ == "__main__": "--load-key", type=str, default="module", - help= - "Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", + help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", ) parser.add_argument( "--use-cpu-offload", @@ -145,20 +128,17 @@ if __name__ == "__main__": parser.add_argument( "--dit-weight", type=str, - default= - "data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", + default="data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", ) parser.add_argument( "--reproduce", action="store_true", - help= - "Enable reproducibility by setting random seeds and deterministic algorithms.", + help="Enable reproducibility by setting random seeds and deterministic algorithms.", ) parser.add_argument( "--disable-autocast", action="store_true", - help= - "Disable autocast for denoising loop and vae decoding in pipeline sampling.", + help="Disable autocast for denoising loop and vae decoding in pipeline sampling.", ) # Flow Matching @@ -167,10 +147,7 @@ if __name__ == "__main__": action="store_true", help="If reverse, learning/sampling from t=1 -> t=0.", ) - parser.add_argument("--flow-solver", - type=str, - default="euler", - help="Solver for flow matching.") + parser.add_argument("--flow-solver", type=str, default="euler", help="Solver for flow matching.") parser.add_argument( "--use-linear-quadratic-schedule", action="store_true", @@ -187,20 +164,11 @@ if __name__ == "__main__": # Model parameters parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill") parser.add_argument("--latent-channels", type=int, default=16) - parser.add_argument("--precision", - type=str, - default="bf16", - choices=["fp32", "fp16", "bf16"]) - parser.add_argument("--rope-theta", - type=int, - default=256, - help="Theta used in RoPE.") + parser.add_argument("--precision", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--rope-theta", type=int, default=256, help="Theta used in RoPE.") parser.add_argument("--vae", type=str, default="884-16c-hy") - parser.add_argument("--vae-precision", - type=str, - default="fp16", - choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--vae-precision", type=str, default="fp16", choices=["fp32", "fp16", "bf16"]) parser.add_argument("--vae-tiling", action="store_true", default=True) parser.add_argument("--vae-sp", action="store_true", default=False) @@ -214,12 +182,8 @@ if __name__ == "__main__": parser.add_argument("--text-states-dim", type=int, default=4096) parser.add_argument("--text-len", type=int, default=256) parser.add_argument("--tokenizer", type=str, default="llm") - parser.add_argument("--prompt-template", - type=str, - default="dit-llm-encode") - parser.add_argument("--prompt-template-video", - type=str, - default="dit-llm-encode-video") + parser.add_argument("--prompt-template", type=str, default="dit-llm-encode") + parser.add_argument("--prompt-template-video", type=str, default="dit-llm-encode-video") parser.add_argument("--hidden-state-skip-layer", type=int, default=2) parser.add_argument("--apply-final-norm", action="store_true") @@ -242,7 +206,5 @@ if __name__ == "__main__": args = parser.parse_args() # process for vae sequence parallel if args.vae_sp and not args.vae_tiling: - raise ValueError( - "Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True." - ) + raise ValueError("Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True.") main(args) diff --git a/fastvideo/sample/sample_t2v_hunyuan_STA.py b/fastvideo/sample/sample_t2v_hunyuan_STA.py index febcf742a..c9481c571 100644 --- a/fastvideo/sample/sample_t2v_hunyuan_STA.py +++ b/fastvideo/sample/sample_t2v_hunyuan_STA.py @@ -13,8 +13,7 @@ from einops import rearrange from fastvideo.models.hunyuan.inference import HunyuanVideoSampler from fastvideo.models.hunyuan.modules.modulate_layers import modulate -from fastvideo.utils.parallel_states import ( - initialize_sequence_parallel_state, nccl_info) +from fastvideo.utils.parallel_states import (initialize_sequence_parallel_state, nccl_info) def teacache_forward( @@ -31,9 +30,7 @@ def teacache_forward( guidance=None, ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: if guidance is None: - guidance = torch.tensor([6016.0], - device=hidden_states.device, - dtype=torch.bfloat16) + guidance = torch.tensor([6016.0], device=hidden_states.device, dtype=torch.bfloat16) img = x = hidden_states text_mask = encoder_attention_mask @@ -57,8 +54,7 @@ def teacache_forward( # guidance modulation if self.guidance_embed: if guidance is None: - raise ValueError( - "Didn't get guidance strength for guidance distilled model.") + raise ValueError("Didn't get guidance strength for guidance distilled model.") # our timestep_embedding is merged into guidance_in(TimestepEmbedder) vec = vec + self.guidance_in(guidance) @@ -68,11 +64,9 @@ def teacache_forward( if self.text_projection == "linear": txt = self.txt_in(txt) elif self.text_projection == "single_refiner": - txt = self.txt_in(txt, t, - text_mask if self.use_attention_mask else None) + txt = self.txt_in(txt, t, text_mask if self.use_attention_mask else None) else: - raise NotImplementedError( - f"Unsupported text_projection: {self.text_projection}") + raise NotImplementedError(f"Unsupported text_projection: {self.text_projection}") txt_seq_len = txt.shape[1] img_seq_len = img.shape[1] @@ -91,17 +85,12 @@ def teacache_forward( img_mod2_gate, ) = self.double_blocks[0].img_mod(vec_).chunk(6, dim=-1) normed_inp = self.double_blocks[0].img_norm1(inp) - modulated_inp = modulate(normed_inp, - shift=img_mod1_shift, - scale=img_mod1_scale) + modulated_inp = modulate(normed_inp, shift=img_mod1_shift, scale=img_mod1_scale) if self.cnt == 0 or self.cnt == self.num_steps - 1: should_calc = True self.accumulated_rel_l1_distance = 0 else: - coefficients = [ - 7.33226126e+02, -4.01131952e+02, 6.75869174e+01, - -3.14987800e+00, 9.61237896e-02 - ] + coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02] rescale_func = np.poly1d(coefficients) self.accumulated_rel_l1_distance += rescale_func( ((modulated_inp - self.previous_modulated_input).abs().mean() / @@ -122,9 +111,7 @@ def teacache_forward( ori_img = img.clone() # --------------------- Pass through DiT blocks ------------------------ for index, block in enumerate(self.double_blocks): - double_block_args = [ - img, txt, vec, freqs_cis, text_mask, mask_strategy[index] - ] + double_block_args = [img, txt, vec, freqs_cis, text_mask, mask_strategy[index]] img, txt = block(*double_block_args) # Merge txt and img to pass through single stream blocks. @@ -150,9 +137,7 @@ def teacache_forward( else: # --------------------- Pass through DiT blocks ------------------------ for index, block in enumerate(self.double_blocks): - double_block_args = [ - img, txt, vec, freqs_cis, text_mask, mask_strategy[index] - ] + double_block_args = [img, txt, vec, freqs_cis, text_mask, mask_strategy[index]] img, txt = block(*double_block_args) # Merge txt and img to pass through single stream blocks. x = torch.cat((img, txt), 1) @@ -191,10 +176,7 @@ def initialize_distributed(): world_size = int(os.getenv("WORLD_SIZE", 1)) print("world_size", world_size) torch.cuda.set_device(local_rank) - dist.init_process_group(backend="nccl", - init_method="env://", - world_size=world_size, - rank=local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) initialize_sequence_parallel_state(world_size) @@ -215,8 +197,7 @@ def main(args): os.makedirs(os.path.dirname(save_path), exist_ok=True) # Load models - hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained( - models_root_path, args=args) + hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(models_root_path, args=args) # Get the updated args args = hunyuan_video_sampler.args @@ -233,8 +214,11 @@ def main(args): with open(args.mask_strategy_file_path, 'r') as f: mask_strategy = json.load(f) - with open(args.prompt) as f: - prompts = [line.strip() for line in f.readlines()] + if args.prompt.endswith('.txt'): + with open(args.prompt) as f: + prompts = [line.strip() for line in f.readlines()] + else: + prompts = [args.prompt] for prompt in prompts: outputs = hunyuan_video_sampler.predict( @@ -259,9 +243,7 @@ def main(args): x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) outputs.append((x * 255).numpy().astype(np.uint8)) os.makedirs(os.path.dirname(args.output_path), exist_ok=True) - imageio.mimsave(os.path.join(args.output_path, f"{prompt[:100]}.mp4"), - outputs, - fps=args.fps) + imageio.mimsave(os.path.join(args.output_path, f"{prompt[:100]}.mp4"), outputs, fps=args.fps) if __name__ == "__main__": @@ -290,14 +272,8 @@ if __name__ == "__main__": default="flow", help="Denoise type for noised inputs.", ) - parser.add_argument("--seed", - type=int, - default=None, - help="Seed for evaluation.") - parser.add_argument("--neg_prompt", - type=str, - default=None, - help="Negative prompt for sampling.") + parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.") + parser.add_argument("--neg_prompt", type=str, default=None, help="Negative prompt for sampling.") parser.add_argument( "--guidance_scale", type=float, @@ -310,14 +286,8 @@ if __name__ == "__main__": default=6.0, help="Embedded classifier free guidance scale.", ) - parser.add_argument("--flow_shift", - type=int, - default=7, - help="Flow shift parameter.") - parser.add_argument("--batch_size", - type=int, - default=1, - help="Batch size for inference.") + parser.add_argument("--flow_shift", type=int, default=7, help="Flow shift parameter.") + parser.add_argument("--batch_size", type=int, default=1, help="Batch size for inference.") parser.add_argument( "--num_videos", type=int, @@ -328,8 +298,7 @@ if __name__ == "__main__": "--load-key", type=str, default="module", - help= - "Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", + help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", ) parser.add_argument( "--use-cpu-offload", @@ -339,20 +308,17 @@ if __name__ == "__main__": parser.add_argument( "--dit-weight", type=str, - default= - "data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", + default="data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", ) parser.add_argument( "--reproduce", action="store_true", - help= - "Enable reproducibility by setting random seeds and deterministic algorithms.", + help="Enable reproducibility by setting random seeds and deterministic algorithms.", ) parser.add_argument( "--disable-autocast", action="store_true", - help= - "Disable autocast for denoising loop and vae decoding in pipeline sampling.", + help="Disable autocast for denoising loop and vae decoding in pipeline sampling.", ) # Flow Matching @@ -361,10 +327,7 @@ if __name__ == "__main__": action="store_true", help="If reverse, learning/sampling from t=1 -> t=0.", ) - parser.add_argument("--flow-solver", - type=str, - default="euler", - help="Solver for flow matching.") + parser.add_argument("--flow-solver", type=str, default="euler", help="Solver for flow matching.") parser.add_argument( "--use-linear-quadratic-schedule", action="store_true", @@ -381,20 +344,11 @@ if __name__ == "__main__": # Model parameters parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill") parser.add_argument("--latent-channels", type=int, default=16) - parser.add_argument("--precision", - type=str, - default="bf16", - choices=["fp32", "fp16", "bf16"]) - parser.add_argument("--rope-theta", - type=int, - default=256, - help="Theta used in RoPE.") + parser.add_argument("--precision", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--rope-theta", type=int, default=256, help="Theta used in RoPE.") parser.add_argument("--vae", type=str, default="884-16c-hy") - parser.add_argument("--vae-precision", - type=str, - default="fp16", - choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--vae-precision", type=str, default="fp16", choices=["fp32", "fp16", "bf16"]) parser.add_argument("--vae-tiling", action="store_true", default=True) parser.add_argument("--vae-sp", action="store_true", default=False) @@ -408,12 +362,8 @@ if __name__ == "__main__": parser.add_argument("--text-states-dim", type=int, default=4096) parser.add_argument("--text-len", type=int, default=256) parser.add_argument("--tokenizer", type=str, default="llm") - parser.add_argument("--prompt-template", - type=str, - default="dit-llm-encode") - parser.add_argument("--prompt-template-video", - type=str, - default="dit-llm-encode-video") + parser.add_argument("--prompt-template", type=str, default="dit-llm-encode") + parser.add_argument("--prompt-template-video", type=str, default="dit-llm-encode-video") parser.add_argument("--hidden-state-skip-layer", type=int, default=2) parser.add_argument("--apply-final-norm", action="store_true") @@ -430,8 +380,7 @@ if __name__ == "__main__": parser.add_argument("--skip_time_steps", type=int, default=10) parser.add_argument( "--mask_strategy_selected", - type=lambda x: [int(i) for i in x.strip('[]').split(',') - ], # Convert string to list of integers + type=lambda x: [int(i) for i in x.strip('[]').split(',')], # Convert string to list of integers default=[1, 2, 6], # Now can be directly set as a list help="order of candidates") parser.add_argument( @@ -450,13 +399,9 @@ if __name__ == "__main__": action="store_true", help="Use torch.compile for speeding up STA inference without teacache", ) - parser.add_argument("--mask_strategy_file_path", - type=str, - default="assets/mask_strategy.json") + parser.add_argument("--mask_strategy_file_path", type=str, default="assets/mask_strategy.json") args = parser.parse_args() # process for vae sequence parallel if args.vae_sp and not args.vae_tiling: - raise ValueError( - "Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True." - ) + raise ValueError("Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True.") main(args) diff --git a/fastvideo/sample/sample_t2v_hunyuan_hf.py b/fastvideo/sample/sample_t2v_hunyuan_hf.py index 58eeb8b8e..b946e6f7f 100644 --- a/fastvideo/sample/sample_t2v_hunyuan_hf.py +++ b/fastvideo/sample/sample_t2v_hunyuan_hf.py @@ -11,8 +11,7 @@ from diffusers.utils import export_to_video from fastvideo.models.hunyuan_hf.modeling_hunyuan import \ HunyuanVideoTransformer3DModel from fastvideo.models.hunyuan_hf.pipeline_hunyuan import HunyuanVideoPipeline -from fastvideo.utils.parallel_states import ( - initialize_sequence_parallel_state, nccl_info) +from fastvideo.utils.parallel_states import (initialize_sequence_parallel_state, nccl_info) def initialize_distributed(): @@ -21,10 +20,7 @@ def initialize_distributed(): world_size = int(os.getenv("WORLD_SIZE", 1)) print("world_size", world_size) torch.cuda.set_device(local_rank) - dist.init_process_group(backend="nccl", - init_method="env://", - world_size=world_size, - rank=local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) initialize_sequence_parallel_state(world_size) @@ -36,35 +32,27 @@ def inference(args): weight_dtype = torch.bfloat16 if args.transformer_path is not None: - transformer = HunyuanVideoTransformer3DModel.from_pretrained( - args.transformer_path) + transformer = HunyuanVideoTransformer3DModel.from_pretrained(args.transformer_path) else: - transformer = HunyuanVideoTransformer3DModel.from_pretrained( - args.model_path, - subfolder="transformer/", - torch_dtype=weight_dtype) + transformer = HunyuanVideoTransformer3DModel.from_pretrained(args.model_path, + subfolder="transformer/", + torch_dtype=weight_dtype) - pipe = HunyuanVideoPipeline.from_pretrained(args.model_path, - transformer=transformer, - torch_dtype=weight_dtype) + pipe = HunyuanVideoPipeline.from_pretrained(args.model_path, transformer=transformer, torch_dtype=weight_dtype) pipe.enable_vae_tiling() if args.lora_checkpoint_dir is not None: print(f"Loading LoRA weights from {args.lora_checkpoint_dir}") - config_path = os.path.join(args.lora_checkpoint_dir, - "lora_config.json") + config_path = os.path.join(args.lora_checkpoint_dir, "lora_config.json") with open(config_path, "r") as f: lora_config_dict = json.load(f) rank = lora_config_dict["lora_params"]["lora_rank"] lora_alpha = lora_config_dict["lora_params"]["lora_alpha"] lora_scaling = lora_alpha / rank - pipe.load_lora_weights(args.lora_checkpoint_dir, - adapter_name="default") + pipe.load_lora_weights(args.lora_checkpoint_dir, adapter_name="default") pipe.set_adapters(["default"], [lora_scaling]) - print( - f"Successfully Loaded LoRA weights from {args.lora_checkpoint_dir}" - ) + print(f"Successfully Loaded LoRA weights from {args.lora_checkpoint_dir}") if args.cpu_offload: pipe.enable_model_cpu_offload(device) else: @@ -73,13 +61,10 @@ def inference(args): # Generate videos from the input prompt if args.prompt_embed_path is not None: - prompt_embeds = (torch.load(args.prompt_embed_path, - map_location="cpu", + prompt_embeds = (torch.load(args.prompt_embed_path, map_location="cpu", weights_only=True).to(device).unsqueeze(0)) - encoder_attention_mask = (torch.load( - args.encoder_attention_mask_path, - map_location="cpu", - weights_only=True).to(device).unsqueeze(0)) + encoder_attention_mask = (torch.load(args.encoder_attention_mask_path, map_location="cpu", + weights_only=True).to(device).unsqueeze(0)) prompts = None elif args.prompt_path is not None: prompts = [line.strip() for line in open(args.prompt_path, "r")] @@ -133,52 +118,44 @@ def inference_quantization(args): model_id = args.model_path if args.quantization == "nf4": - quantization_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_compute_dtype=torch.bfloat16, - bnb_4bit_quant_type="nf4", - llm_int8_skip_modules=["proj_out", "norm_out"]) - transformer = HunyuanVideoTransformer3DModel.from_pretrained( - model_id, - subfolder="transformer/", - torch_dtype=torch.bfloat16, - quantization_config=quantization_config) + quantization_config = BitsAndBytesConfig(load_in_4bit=True, + bnb_4bit_compute_dtype=torch.bfloat16, + bnb_4bit_quant_type="nf4", + llm_int8_skip_modules=["proj_out", "norm_out"]) + transformer = HunyuanVideoTransformer3DModel.from_pretrained(model_id, + subfolder="transformer/", + torch_dtype=torch.bfloat16, + quantization_config=quantization_config) if args.quantization == "int8": - quantization_config = BitsAndBytesConfig( - load_in_8bit=True, llm_int8_skip_modules=["proj_out", "norm_out"]) - transformer = HunyuanVideoTransformer3DModel.from_pretrained( - model_id, - subfolder="transformer/", - torch_dtype=torch.bfloat16, - quantization_config=quantization_config) + quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_skip_modules=["proj_out", "norm_out"]) + transformer = HunyuanVideoTransformer3DModel.from_pretrained(model_id, + subfolder="transformer/", + torch_dtype=torch.bfloat16, + quantization_config=quantization_config) elif not args.quantization: - transformer = HunyuanVideoTransformer3DModel.from_pretrained( - model_id, subfolder="transformer/", - torch_dtype=torch.bfloat16).to(device) + transformer = HunyuanVideoTransformer3DModel.from_pretrained(model_id, + subfolder="transformer/", + torch_dtype=torch.bfloat16).to(device) - print("Max vram for read transformer:", - round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3), - "GiB") + print("Max vram for read transformer:", round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3), "GiB") torch.cuda.reset_max_memory_allocated(device) if not args.cpu_offload: - pipe = HunyuanVideoPipeline.from_pretrained( - model_id, torch_dtype=torch.bfloat16).to(device) + pipe = HunyuanVideoPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(device) pipe.transformer = transformer else: - pipe = HunyuanVideoPipeline.from_pretrained(model_id, - transformer=transformer, - torch_dtype=torch.bfloat16) + pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.bfloat16) torch.cuda.reset_max_memory_allocated(device) pipe.scheduler._shift = args.flow_shift pipe.vae.enable_tiling() if args.cpu_offload: pipe.enable_model_cpu_offload() - print("Max vram for init pipeline:", - round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3), - "GiB") - with open(args.prompt) as f: - prompts = f.readlines() + print("Max vram for init pipeline:", round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3), "GiB") + if args.prompt.endswith('.txt'): + with open(args.prompt) as f: + prompts = [line.strip() for line in f.readlines()] + else: + prompts = [args.prompt] generator = torch.Generator("cpu").manual_seed(args.seed) os.makedirs(os.path.dirname(args.output_path), exist_ok=True) @@ -193,14 +170,9 @@ def inference_quantization(args): num_inference_steps=args.num_inference_steps, generator=generator, ).frames[0] - export_to_video(output, - os.path.join(args.output_path, f"{prompt[:100]}.mp4"), - fps=args.fps) + export_to_video(output, os.path.join(args.output_path, f"{prompt[:100]}.mp4"), fps=args.fps) print("Time:", round(time.perf_counter() - start_time, 2), "seconds") - print( - "Max vram for denoise:", - round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3), - "GiB") + print("Max vram for denoise:", round(torch.cuda.max_memory_allocated(device="cuda") / 1024**3, 3), "GiB") if __name__ == "__main__": @@ -233,14 +205,8 @@ if __name__ == "__main__": default="flow", help="Denoise type for noised inputs.", ) - parser.add_argument("--seed", - type=int, - default=None, - help="Seed for evaluation.") - parser.add_argument("--neg_prompt", - type=str, - default=None, - help="Negative prompt for sampling.") + parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.") + parser.add_argument("--neg_prompt", type=str, default=None, help="Negative prompt for sampling.") parser.add_argument( "--guidance_scale", type=float, @@ -253,14 +219,8 @@ if __name__ == "__main__": default=6.0, help="Embedded classifier free guidance scale.", ) - parser.add_argument("--flow_shift", - type=int, - default=7, - help="Flow shift parameter.") - parser.add_argument("--batch_size", - type=int, - default=1, - help="Batch size for inference.") + parser.add_argument("--flow_shift", type=int, default=7, help="Flow shift parameter.") + parser.add_argument("--batch_size", type=int, default=1, help="Batch size for inference.") parser.add_argument( "--num_videos", type=int, @@ -271,26 +231,22 @@ if __name__ == "__main__": "--load-key", type=str, default="module", - help= - "Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", + help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", ) parser.add_argument( "--dit-weight", type=str, - default= - "data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", + default="data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", ) parser.add_argument( "--reproduce", action="store_true", - help= - "Enable reproducibility by setting random seeds and deterministic algorithms.", + help="Enable reproducibility by setting random seeds and deterministic algorithms.", ) parser.add_argument( "--disable-autocast", action="store_true", - help= - "Disable autocast for denoising loop and vae decoding in pipeline sampling.", + help="Disable autocast for denoising loop and vae decoding in pipeline sampling.", ) # Flow Matching @@ -299,10 +255,7 @@ if __name__ == "__main__": action="store_true", help="If reverse, learning/sampling from t=1 -> t=0.", ) - parser.add_argument("--flow-solver", - type=str, - default="euler", - help="Solver for flow matching.") + parser.add_argument("--flow-solver", type=str, default="euler", help="Solver for flow matching.") parser.add_argument( "--use-linear-quadratic-schedule", action="store_true", @@ -319,20 +272,11 @@ if __name__ == "__main__": # Model parameters parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill") parser.add_argument("--latent-channels", type=int, default=16) - parser.add_argument("--precision", - type=str, - default="bf16", - choices=["fp32", "fp16", "bf16", "fp8"]) - parser.add_argument("--rope-theta", - type=int, - default=256, - help="Theta used in RoPE.") + parser.add_argument("--precision", type=str, default="bf16", choices=["fp32", "fp16", "bf16", "fp8"]) + parser.add_argument("--rope-theta", type=int, default=256, help="Theta used in RoPE.") parser.add_argument("--vae", type=str, default="884-16c-hy") - parser.add_argument("--vae-precision", - type=str, - default="fp16", - choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--vae-precision", type=str, default="fp16", choices=["fp32", "fp16", "bf16"]) parser.add_argument("--vae-tiling", action="store_true", default=True) parser.add_argument("--text-encoder", type=str, default="llm") @@ -345,12 +289,8 @@ if __name__ == "__main__": parser.add_argument("--text-states-dim", type=int, default=4096) parser.add_argument("--text-len", type=int, default=256) parser.add_argument("--tokenizer", type=str, default="llm") - parser.add_argument("--prompt-template", - type=str, - default="dit-llm-encode") - parser.add_argument("--prompt-template-video", - type=str, - default="dit-llm-encode-video") + parser.add_argument("--prompt-template", type=str, default="dit-llm-encode") + parser.add_argument("--prompt-template-video", type=str, default="dit-llm-encode-video") parser.add_argument("--hidden-state-skip-layer", type=int, default=2) parser.add_argument("--apply-final-norm", action="store_true") diff --git a/fastvideo/sample/sample_t2v_mochi.py b/fastvideo/sample/sample_t2v_mochi.py index 085c10ccd..c81fd1697 100644 --- a/fastvideo/sample/sample_t2v_mochi.py +++ b/fastvideo/sample/sample_t2v_mochi.py @@ -10,8 +10,7 @@ from diffusers.utils import export_to_video from fastvideo.distill.solver import PCMFMScheduler from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline -from fastvideo.utils.parallel_states import ( - initialize_sequence_parallel_state, nccl_info) +from fastvideo.utils.parallel_states import (initialize_sequence_parallel_state, nccl_info) def initialize_distributed(): @@ -19,10 +18,7 @@ def initialize_distributed(): world_size = int(os.getenv("WORLD_SIZE", 1)) print("world_size", world_size) torch.cuda.set_device(local_rank) - dist.init_process_group(backend="nccl", - init_method="env://", - world_size=world_size, - rank=local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) initialize_sequence_parallel_state(world_size) @@ -45,33 +41,25 @@ def main(args): args.linear_range, ) if args.transformer_path is not None: - transformer = MochiTransformer3DModel.from_pretrained( - args.transformer_path) + transformer = MochiTransformer3DModel.from_pretrained(args.transformer_path) else: - transformer = MochiTransformer3DModel.from_pretrained( - args.model_path, subfolder="transformer/") + transformer = MochiTransformer3DModel.from_pretrained(args.model_path, subfolder="transformer/") - pipe = MochiPipeline.from_pretrained(args.model_path, - transformer=transformer, - scheduler=scheduler) + pipe = MochiPipeline.from_pretrained(args.model_path, transformer=transformer, scheduler=scheduler) pipe.enable_vae_tiling() if args.lora_checkpoint_dir is not None: print(f"Loading LoRA weights from {args.lora_checkpoint_dir}") - config_path = os.path.join(args.lora_checkpoint_dir, - "lora_config.json") + config_path = os.path.join(args.lora_checkpoint_dir, "lora_config.json") with open(config_path, "r") as f: lora_config_dict = json.load(f) rank = lora_config_dict["lora_params"]["lora_rank"] lora_alpha = lora_config_dict["lora_params"]["lora_alpha"] lora_scaling = lora_alpha / rank - pipe.load_lora_weights(args.lora_checkpoint_dir, - adapter_name="default") + pipe.load_lora_weights(args.lora_checkpoint_dir, adapter_name="default") pipe.set_adapters(["default"], [lora_scaling]) - print( - f"Successfully Loaded LoRA weights from {args.lora_checkpoint_dir}" - ) + print(f"Successfully Loaded LoRA weights from {args.lora_checkpoint_dir}") # pipe.to(device) pipe.enable_model_cpu_offload(device) @@ -79,13 +67,10 @@ def main(args): # Generate videos from the input prompt if args.prompt_embed_path is not None: - prompt_embeds = (torch.load(args.prompt_embed_path, - map_location="cpu", + prompt_embeds = (torch.load(args.prompt_embed_path, map_location="cpu", weights_only=True).to(device).unsqueeze(0)) - encoder_attention_mask = (torch.load( - args.encoder_attention_mask_path, - map_location="cpu", - weights_only=True).to(device).unsqueeze(0)) + encoder_attention_mask = (torch.load(args.encoder_attention_mask_path, map_location="cpu", + weights_only=True).to(device).unsqueeze(0)) prompts = None elif args.prompt_path is not None: prompts = [line.strip() for line in open(args.prompt_path, "r")] @@ -151,9 +136,7 @@ if __name__ == "__main__": parser.add_argument("--prompt_embed_path", type=str, default=None) parser.add_argument("--prompt_path", type=str, default=None) parser.add_argument("--scheduler_type", type=str, default="euler") - parser.add_argument("--encoder_attention_mask_path", - type=str, - default=None) + parser.add_argument("--encoder_attention_mask_path", type=str, default=None) parser.add_argument( "--lora_checkpoint_dir", type=str, diff --git a/fastvideo/sample/sample_t2v_mochi_no_sp.py b/fastvideo/sample/sample_t2v_mochi_no_sp.py index 2a1e702bb..72be0ff3f 100644 --- a/fastvideo/sample/sample_t2v_mochi_no_sp.py +++ b/fastvideo/sample/sample_t2v_mochi_no_sp.py @@ -14,14 +14,10 @@ def main(args): # do not invert scheduler = FlowMatchEulerDiscreteScheduler() if args.transformer_path is not None: - transformer = MochiTransformer3DModel.from_pretrained( - args.transformer_path) + transformer = MochiTransformer3DModel.from_pretrained(args.transformer_path) else: - transformer = MochiTransformer3DModel.from_pretrained( - args.model_path, subfolder="transformer/") - pipe = MochiPipeline.from_pretrained(args.model_path, - transformer=transformer, - scheduler=scheduler) + transformer = MochiTransformer3DModel.from_pretrained(args.model_path, subfolder="transformer/") + pipe = MochiPipeline.from_pretrained(args.model_path, transformer=transformer, scheduler=scheduler) pipe.enable_vae_tiling() # pipe.to("cuda:1") pipe.enable_model_cpu_offload() diff --git a/fastvideo/sample/sample_t2v_stepvideo.py b/fastvideo/sample/sample_t2v_stepvideo.py new file mode 100644 index 000000000..aa5a42fdf --- /dev/null +++ b/fastvideo/sample/sample_t2v_stepvideo.py @@ -0,0 +1,248 @@ +import argparse +import os + +import torch +import torch.distributed as dist +import torch.nn as nn + +from fastvideo.models.stepvideo.diffusion.scheduler import \ + FlowMatchDiscreteScheduler +from fastvideo.models.stepvideo.diffusion.video_pipeline import \ + StepVideoPipeline +from fastvideo.models.stepvideo.modules.model import StepVideoModel +from fastvideo.models.stepvideo.utils import setup_seed +from fastvideo.models.stepvideo.utils.quantization import (convert_fp8_linear, fp8_linear_forward) +from fastvideo.utils.logging_ import main_print +from fastvideo.utils.parallel_states import (initialize_sequence_parallel_state, nccl_info) + + +def initialize_distributed(): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + local_rank = int(os.getenv("RANK", 0)) + world_size = int(os.getenv("WORLD_SIZE", 1)) + print("world_size", world_size) + torch.cuda.set_device(local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) + initialize_sequence_parallel_state(world_size) + + +def parse_args(namespace=None): + parser = argparse.ArgumentParser(description="StepVideo inference script") + + parser = add_extra_models_args(parser) + parser = add_denoise_schedule_args(parser) + parser = add_inference_args(parser) + + args = parser.parse_args(namespace=namespace) + + return args + + +def add_extra_models_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Extra models args, including vae, text encoders and tokenizers)") + + group.add_argument( + "--vae_url", + type=str, + default='127.0.0.1', + help="vae url.", + ) + group.add_argument( + "--caption_url", + type=str, + default='127.0.0.1', + help="caption url.", + ) + + return parser + + +def add_denoise_schedule_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Denoise schedule args") + + # Flow Matching + group.add_argument( + "--time_shift", + type=float, + default=13, + help="Shift factor for flow matching schedulers.", + ) + group.add_argument( + "--flow_reverse", + action="store_true", + help="If reverse, learning/sampling from t=1 -> t=0.", + ) + group.add_argument( + "--flow_solver", + type=str, + default="euler", + help="Solver for flow matching.", + ) + + return parser + + +def add_inference_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Inference args") + + # ======================== Model loads ======================== + group.add_argument( + "--model_dir", + type=str, + default="./ckpts", + help="Root path of all the models, including t2v models and extra models.", + ) + group.add_argument( + "--model_resolution", + type=str, + default="540p", + choices=["540p"], + help="Root path of all the models, including t2v models and extra models.", + ) + group.add_argument( + "--use-cpu-offload", + action="store_true", + help="Use CPU offload for the model load.", + ) + group.add_argument( + "--use-fp8", + action="store_true", + help="FP8 Quantization for single GPU support.", + ) + + # ======================== Inference general setting ======================== + group.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size for inference and evaluation.", + ) + group.add_argument( + "--infer_steps", + type=int, + default=50, + help="Number of denoising steps for inference.", + ) + group.add_argument( + "--save_path", + type=str, + default="./results", + help="Path to save the generated samples.", + ) + group.add_argument( + "--name_suffix", + type=str, + default="", + help="Suffix for the names of saved samples.", + ) + group.add_argument( + "--num_videos", + type=int, + default=1, + help="Number of videos to generate for each prompt.", + ) + # ---sample size--- + group.add_argument( + "--num_frames", + type=int, + default=204, + help="How many frames to sample from a video. ", + ) + group.add_argument( + "--height", + type=int, + default=768, + help="The height of video sample", + ) + group.add_argument( + "--width", + type=int, + default=768, + help="The width of video sample", + ) + # --- prompt --- + group.add_argument( + "--prompt", + type=str, + default=None, + help="Prompt for sampling during evaluation.", + ) + group.add_argument("--seed", type=int, default=1234, help="Seed for evaluation.") + + # Classifier-Free Guidance + group.add_argument("--pos_magic", + type=str, + default="超高清、HDR 视频、环境光、杜比全景声、画面稳定、流畅动作、逼真的细节、专业级构图、超现实主义、自然、生动、超细节、清晰。", + help="Positive magic prompt for sampling.") + group.add_argument("--neg_magic", + type=str, + default="画面暗、低分辨率、不良手、文本、缺少手指、多余的手指、裁剪、低质量、颗粒状、签名、水印、用户名、模糊。", + help="Negative magic prompt for sampling.") + group.add_argument("--cfg_scale", type=float, default=9.0, help="Classifier free guidance scale.") + + return parser + + +if __name__ == "__main__": + args = parse_args() + initialize_distributed() + main_print(f"sequence parallel size: {nccl_info.sp_size}") + device = torch.cuda.current_device() + + setup_seed(args.seed) + main_print("Loading model, this might take a while...") + scheduler = FlowMatchDiscreteScheduler() + + if args.use_fp8: + assert int(os.getenv("WORLD_SIZE", 1)) == 1 + transformer = StepVideoModel.from_pretrained(os.path.join(args.model_dir, "transformer"), + torch_dtype=torch.bfloat16, + device="cpu") + if not os.path.exists(args.model_dir + "/fp8_transformer.pth"): + print("no_fp8 weight, creating...") + scale_dict = convert_fp8_linear(transformer, torch.bfloat16) + torch.save(transformer.state_dict(), args.model_dir + "/fp8_transformer.pth") + torch.save(scale_dict, args.model_dir + "/fp8_scale_dict.pth") + else: + transformer.load_state_dict(torch.load(args.model_dir + "/fp8_transformer.pth")) + scale_dict = torch.load(args.model_dir + "/fp8_scale_dict.pth") + original_dtype = torch.bfloat16 + for key, layer in transformer.named_modules(): + if isinstance(layer, nn.Linear) and 'transformer_blocks' in key and key in scale_dict: + layer.weight.data = layer.weight.data.to(torch.float8_e4m3fn) + print(f"{key}, layer.weight.dtype: {layer.weight.dtype}") + original_forward = layer.forward + scale = scale_dict[key] + setattr(layer, "fp8_scale", scale.to(dtype=original_dtype)) + setattr(layer, "original_forward", original_forward) + setattr(layer, "forward", lambda input, m=layer: fp8_linear_forward(m, original_dtype, input)) + else: + transformer = StepVideoModel.from_pretrained(os.path.join(args.model_dir, "transformer"), + torch_dtype=torch.bfloat16, + device=device) + + transformer = transformer.to(device) + pipeline = StepVideoPipeline(transformer, scheduler, save_path=args.save_path) + + pipeline.setup_api( + vae_url=args.vae_url, + caption_url=args.caption_url, + ) + if args.prompt.endswith('.txt'): + with open(args.prompt) as f: + prompts = [line.strip() for line in f.readlines()] + else: + prompts = [args.prompt] + for prompt in prompts: + videos = pipeline(prompt=prompt, + num_frames=args.num_frames, + height=args.height, + width=args.width, + num_inference_steps=args.infer_steps, + guidance_scale=args.cfg_scale, + time_shift=args.time_shift, + pos_magic=args.pos_magic, + neg_magic=args.neg_magic, + output_file_name=prompt[:50]) + + dist.destroy_process_group() diff --git a/fastvideo/sample/sample_t2v_stepvideo_STA.py b/fastvideo/sample/sample_t2v_stepvideo_STA.py new file mode 100644 index 000000000..54b5aaaf8 --- /dev/null +++ b/fastvideo/sample/sample_t2v_stepvideo_STA.py @@ -0,0 +1,376 @@ +import argparse +import json +import os +import types +from typing import Dict, Optional + +import numpy as np +import torch +import torch.distributed as dist +from einops import rearrange, repeat + +from fastvideo.models.stepvideo.diffusion.scheduler import \ + FlowMatchDiscreteScheduler +from fastvideo.models.stepvideo.diffusion.video_pipeline import \ + StepVideoPipeline +from fastvideo.models.stepvideo.modules.model import StepVideoModel +from fastvideo.models.stepvideo.utils import setup_seed +from fastvideo.utils.logging_ import main_print +from fastvideo.utils.parallel_states import (initialize_sequence_parallel_state, nccl_info) + + +def initialize_distributed(): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + local_rank = int(os.getenv("RANK", 0)) + world_size = int(os.getenv("WORLD_SIZE", 1)) + main_print(f"world_size: {world_size}") + torch.cuda.set_device(local_rank) + dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank) + initialize_sequence_parallel_state(world_size) + + +def parse_args(namespace=None): + parser = argparse.ArgumentParser(description="StepVideo inference script") + + parser = add_extra_models_args(parser) + parser = add_denoise_schedule_args(parser) + parser = add_inference_args(parser) + + args = parser.parse_args(namespace=namespace) + + return args + + +def add_extra_models_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Extra models args, including vae, text encoders and tokenizers)") + + group.add_argument( + "--vae_url", + type=str, + default='127.0.0.1', + help="vae url.", + ) + group.add_argument( + "--caption_url", + type=str, + default='127.0.0.1', + help="caption url.", + ) + + return parser + + +def add_denoise_schedule_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Denoise schedule args") + + # Flow Matching + group.add_argument( + "--time_shift", + type=float, + default=13, + help="Shift factor for flow matching schedulers.", + ) + group.add_argument( + "--flow_reverse", + action="store_true", + help="If reverse, learning/sampling from t=1 -> t=0.", + ) + group.add_argument( + "--flow_solver", + type=str, + default="euler", + help="Solver for flow matching.", + ) + + return parser + + +def add_inference_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Inference args") + + # ======================== Model loads ======================== + group.add_argument( + "--model_dir", + type=str, + default="./ckpts", + help="Root path of all the models, including t2v models and extra models.", + ) + group.add_argument( + "--model_resolution", + type=str, + default="540p", + choices=["540p"], + help="Root path of all the models, including t2v models and extra models.", + ) + group.add_argument( + "--use-cpu-offload", + action="store_true", + help="Use CPU offload for the model load.", + ) + + # ======================== Inference general setting ======================== + group.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size for inference and evaluation.", + ) + group.add_argument( + "--infer_steps", + type=int, + default=50, + help="Number of denoising steps for inference.", + ) + group.add_argument( + "--save_path", + type=str, + default="./results", + help="Path to save the generated samples.", + ) + group.add_argument( + "--name_suffix", + type=str, + default="", + help="Suffix for the names of saved samples.", + ) + group.add_argument( + "--num_videos", + type=int, + default=1, + help="Number of videos to generate for each prompt.", + ) + # ---sample size--- + group.add_argument( + "--num_frames", + type=int, + default=204, + help="How many frames to sample from a video. ", + ) + group.add_argument( + "--height", + type=int, + default=768, + help="The height of video sample", + ) + group.add_argument( + "--width", + type=int, + default=768, + help="The width of video sample", + ) + # --- prompt --- + group.add_argument( + "--prompt", + type=str, + default=None, + help="Prompt for sampling during evaluation.", + ) + group.add_argument("--seed", type=int, default=1234, help="Seed for evaluation.") + + # Classifier-Free Guidance + group.add_argument("--pos_magic", + type=str, + default="超高清、HDR 视频、环境光、杜比全景声、画面稳定、流畅动作、逼真的细节、专业级构图、超现实主义、自然、生动、超细节、清晰。", + help="Positive magic prompt for sampling.") + group.add_argument("--neg_magic", + type=str, + default="画面暗、低分辨率、不良手、文本、缺少手指、多余的手指、裁剪、低质量、颗粒状、签名、水印、用户名、模糊。", + help="Negative magic prompt for sampling.") + group.add_argument("--cfg_scale", type=float, default=9.0, help="Classifier free guidance scale.") + group.add_argument("--mask_search_files_path", type=str, default="assets/mask_strategy.json") + group.add_argument("--mask_strategy_file_path", type=str, default="assets/mask_strategy_stepvideo.json") + group.add_argument("--skip_time_steps", type=int, default=10) + group.add_argument( + "--mask_strategy_selected", + type=lambda x: [int(i) for i in x.strip('[]').split(',')], # Convert string to list of integers + default=[1, 2, 6], # Now can be directly set as a list + help="order of candidates") + parser.add_argument( + "--rel_l1_thresh", + type=float, + default=0, + help="0.22 for 1.67x speedup, 0.23 for 2.1x speedup", + ) + parser.add_argument( + "--enable_teacache", + action="store_true", + help="Use teacache for speeding up inference", + ) + return parser + + +def teacache_forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_hidden_states_2: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + added_cond_kwargs: Dict[str, torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + fps: torch.Tensor = None, + return_dict: bool = True, + mask_strategy=None, +): + assert hidden_states.ndim == 5 + "hidden_states's shape should be (bsz, f, ch, h ,w)" + + bsz, frame, _, height, width = hidden_states.shape + height, width = height // self.patch_size, width // self.patch_size + + hidden_states = self.patchfy(hidden_states) + len_frame = hidden_states.shape[1] + + if self.use_additional_conditions: + added_cond_kwargs = { + "resolution": torch.tensor([(height, width)] * bsz, device=hidden_states.device, dtype=hidden_states.dtype), + "nframe": torch.tensor([frame] * bsz, device=hidden_states.device, dtype=hidden_states.dtype), + "fps": fps + } + else: + added_cond_kwargs = {} + + timestep, embedded_timestep = self.adaln_single(timestep, added_cond_kwargs=added_cond_kwargs) + + encoder_hidden_states = self.caption_projection(self.caption_norm(encoder_hidden_states)) + + if encoder_hidden_states_2 is not None and hasattr(self, 'clip_projection'): + clip_embedding = self.clip_projection(encoder_hidden_states_2) + encoder_hidden_states = torch.cat([clip_embedding, encoder_hidden_states], dim=1) + + hidden_states = rearrange(hidden_states, '(b f) l d-> b (f l) d', b=bsz, f=frame, l=len_frame).contiguous() + + embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame).contiguous() + + shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1) + + encoder_hidden_states, attn_mask = self.prepare_attn_mask(encoder_attention_mask, + encoder_hidden_states, + q_seqlen=frame * len_frame) + + if self.enable_teacache: + hidden_states_ = hidden_states.clone() + + normed_hidden_states = self.transformer_blocks[0].norm1(hidden_states_) + normed_hidden_states = rearrange(normed_hidden_states, 'b (f l) d -> (b f) l d', b=bsz, f=frame, l=len_frame) + + modulated_inp = normed_hidden_states * (1 + scale) + shift + + if self.cnt == 0 or self.cnt == self.num_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = [6.74352814e+03, -2.22814115e+03, 2.55029094e+02, -1.12338285e+01, 2.84921593e-01] + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func( + ((modulated_inp - self.previous_modulated_input).abs().mean() / + self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + # print(f"accumulated_rel_l1_distance: {self.accumulated_rel_l1_distance}") + should_calc = False + else: + # print(f"accumulated_rel_l1_distance: {self.accumulated_rel_l1_distance}") + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.cnt += 1 + if self.cnt == self.num_steps: + self.cnt = 0 + + if self.enable_teacache: + if not should_calc: + # print(f"skip step {self.cnt}") + hidden_states += self.previous_residual + else: + # print(f"calc step {self.cnt}") + ori_hidden_states = hidden_states.clone() + hidden_states = self.block_forward(hidden_states, + encoder_hidden_states, + timestep=timestep, + rope_positions=[frame, height, width], + attn_mask=attn_mask, + parallel=self.parallel, + mask_strategy=mask_strategy) + self.previous_residual = hidden_states - ori_hidden_states + else: + # --------------------- Pass through DiT blocks ------------------------ + hidden_states = self.block_forward(hidden_states, + encoder_hidden_states, + timestep=timestep, + rope_positions=[frame, height, width], + attn_mask=attn_mask, + parallel=self.parallel, + mask_strategy=mask_strategy) + + # ---------------------------- Final layer ------------------------------ + hidden_states = rearrange(hidden_states, 'b (f l) d -> (b f) l d', b=bsz, f=frame, l=len_frame) + + hidden_states = self.norm_out(hidden_states) + # Modulation + hidden_states = hidden_states * (1 + scale) + shift + hidden_states = self.proj_out(hidden_states) + + # unpatchify + hidden_states = hidden_states.reshape(shape=(-1, height, width, self.patch_size, self.patch_size, + self.out_channels)) + + hidden_states = rearrange(hidden_states, 'n h w p q c -> n c h p w q') + output = hidden_states.reshape(shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)) + + output = rearrange(output, '(b f) c h w -> b f c h w', f=frame) + if return_dict: + return {'x': output} + return output + + +if __name__ == "__main__": + args = parse_args() + initialize_distributed() + main_print(f"sequence parallel size: {nccl_info.sp_size}") + device = torch.cuda.current_device() + + setup_seed(args.seed) + main_print("Loading model, this might take a while...") + transformer = StepVideoModel.from_pretrained(os.path.join(args.model_dir, "transformer"), + torch_dtype=torch.bfloat16, + device_map=device) + if args.enable_teacache: + transformer.forward = types.MethodType(teacache_forward, transformer) + scheduler = FlowMatchDiscreteScheduler() + pipeline = StepVideoPipeline(transformer, scheduler, save_path=args.save_path) + pipeline.setup_api( + vae_url=args.vae_url, + caption_url=args.caption_url, + ) + + # TeaCache + pipeline.transformer.__class__.enable_teacache = True + pipeline.transformer.__class__.cnt = 0 + pipeline.transformer.__class__.num_steps = args.infer_steps + pipeline.transformer.__class__.rel_l1_thresh = args.rel_l1_thresh # 0.1 for 1.6x speedup, 0.15 for 2.1x speedup + pipeline.transformer.__class__.accumulated_rel_l1_distance = 0 + pipeline.transformer.__class__.previous_modulated_input = None + pipeline.transformer.__class__.previous_residual = None + + with open(args.mask_strategy_file_path, 'r') as f: + mask_strategy = json.load(f) + + if args.prompt.endswith('.txt'): + with open(args.prompt) as f: + prompts = [line.strip() for line in f.readlines()] + else: + prompts = [args.prompt] + for prompt in prompts: + main_print(f"Generating video for prompt: {prompt}") + videos = pipeline(prompt=prompt, + num_frames=args.num_frames, + height=args.height, + width=args.width, + num_inference_steps=args.infer_steps, + guidance_scale=args.cfg_scale, + time_shift=args.time_shift, + pos_magic=args.pos_magic, + neg_magic=args.neg_magic, + output_file_name=prompt[:150], + mask_strategy=mask_strategy) + + dist.destroy_process_group() diff --git a/fastvideo/train.py b/fastvideo/train.py index 5878b9f85..97bf669b5 100644 --- a/fastvideo/train.py +++ b/fastvideo/train.py @@ -19,25 +19,19 @@ from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from tqdm.auto import tqdm -from fastvideo.dataset.latent_datasets import (LatentDataset, - latent_collate_function) +from fastvideo.dataset.latent_datasets import (LatentDataset, latent_collate_function) from fastvideo.models.mochi_hf.mochi_latents_utils import normalize_dit_input from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline from fastvideo.models.hunyuan_hf.pipeline_hunyuan import HunyuanVideoPipeline -from fastvideo.utils.checkpoint import (resume_lora_optimizer, save_checkpoint, - save_lora_checkpoint) -from fastvideo.utils.communications import (broadcast, - sp_parallel_dataloader_wrapper) +from fastvideo.utils.checkpoint import (resume_lora_optimizer, save_checkpoint, save_lora_checkpoint) +from fastvideo.utils.communications import (broadcast, sp_parallel_dataloader_wrapper) from fastvideo.utils.dataset_utils import LengthGroupedSampler -from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing, - get_dit_fsdp_kwargs) +from fastvideo.utils.fsdp_util import (apply_fsdp_checkpointing, get_dit_fsdp_kwargs) from fastvideo.utils.load import load_transformer from fastvideo.utils.logging_ import main_print -from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group, - get_sequence_parallel_state, - initialize_sequence_parallel_state - ) +from fastvideo.utils.parallel_states import (destroy_sequence_parallel_group, get_sequence_parallel_state, + initialize_sequence_parallel_state) from fastvideo.utils.validation import log_validation # Will error if the minimal version of diffusers is not installed. Remove at your own risks. @@ -77,16 +71,11 @@ def compute_density_for_timestep_sampling( return u -def get_sigmas(noise_scheduler, - device, - timesteps, - n_dim=4, - dtype=torch.float32): +def get_sigmas(noise_scheduler, device, timesteps, n_dim=4, dtype=torch.float32): sigmas = noise_scheduler.sigmas.to(device=device, dtype=dtype) schedule_timesteps = noise_scheduler.timesteps.to(device) timesteps = timesteps.to(device) - step_indices = [(schedule_timesteps == t).nonzero().item() - for t in timesteps] + step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < n_dim: @@ -132,8 +121,7 @@ def train_one_step( mode_scale=mode_scale, ) indices = (u * noise_scheduler.config.num_train_timesteps).long() - timesteps = noise_scheduler.timesteps[indices].to( - device=latents.device) + timesteps = noise_scheduler.timesteps[indices].to(device=latents.device) if sp_size > 1: # Make sure that the timesteps are the same across all sp processes. broadcast(timesteps) @@ -154,10 +142,7 @@ def train_one_step( "return_dict": False, } if 'hunyuan' in model_type: - input_kwargs["guidance"] = torch.tensor( - [1000.0], - device=noisy_model_input.device, - dtype=torch.bfloat16) + input_kwargs["guidance"] = torch.tensor([1000.0], device=noisy_model_input.device, dtype=torch.bfloat16) model_pred = transformer(**input_kwargs)[0] if precondition_outputs: @@ -167,8 +152,7 @@ def train_one_step( else: target = noise - latents - loss = (torch.mean((model_pred.float() - target.float())**2) / - gradient_accumulation_steps) + loss = (torch.mean((model_pred.float() - target.float())**2) / gradient_accumulation_steps) loss.backward() @@ -234,32 +218,23 @@ def main(args): transformer.add_adapter(transformer_lora_config) if args.resume_from_lora_checkpoint: - lora_state_dict = pipe.lora_state_dict( - args.resume_from_lora_checkpoint) + lora_state_dict = pipe.lora_state_dict(args.resume_from_lora_checkpoint) transformer_state_dict = { f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.") } - transformer_state_dict = convert_unet_state_dict_to_peft( - transformer_state_dict) - incompatible_keys = set_peft_model_state_dict(transformer, - transformer_state_dict, - adapter_name="default") + transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict) + incompatible_keys = set_peft_model_state_dict(transformer, transformer_state_dict, adapter_name="default") if incompatible_keys is not None: # check only for unexpected keys - unexpected_keys = getattr(incompatible_keys, "unexpected_keys", - None) + unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None) if unexpected_keys: - main_print( - f"Loading adapter weights from state_dict led to unexpected keys not found in the model: " - f" {unexpected_keys}. ") + main_print(f"Loading adapter weights from state_dict led to unexpected keys not found in the model: " + f" {unexpected_keys}. ") main_print( - f" Total training parameters = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e6} M" - ) - main_print( - f"--> Initializing FSDP with sharding strategy: {args.fsdp_sharding_startegy}" - ) + f" Total training parameters = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e6} M") + main_print(f"--> Initializing FSDP with sharding strategy: {args.fsdp_sharding_startegy}") fsdp_kwargs, no_split_modules = get_dit_fsdp_kwargs( transformer, args.fsdp_sharding_startegy, @@ -271,14 +246,9 @@ def main(args): if args.use_lora: transformer.config.lora_rank = args.lora_rank transformer.config.lora_alpha = args.lora_alpha - transformer.config.lora_target_modules = [ - "to_k", "to_q", "to_v", "to_out.0" - ] - transformer._no_split_modules = [ - no_split_module.__name__ for no_split_module in no_split_modules - ] - fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"]( - transformer) + transformer.config.lora_target_modules = ["to_k", "to_q", "to_v", "to_out.0"] + transformer._no_split_modules = [no_split_module.__name__ for no_split_module in no_split_modules] + fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](transformer) transformer = FSDP( transformer, @@ -287,8 +257,7 @@ def main(args): main_print("--> model loaded") if args.gradient_checkpointing: - apply_fsdp_checkpointing(transformer, no_split_modules, - args.selective_checkpointing) + apply_fsdp_checkpointing(transformer, no_split_modules, args.selective_checkpointing) # Set model as trainable. transformer.train() @@ -296,8 +265,7 @@ def main(args): noise_scheduler = FlowMatchEulerDiscreteScheduler() params_to_optimize = transformer.parameters() - params_to_optimize = list( - filter(lambda p: p.requires_grad, params_to_optimize)) + params_to_optimize = list(filter(lambda p: p.requires_grad, params_to_optimize)) optimizer = torch.optim.AdamW( params_to_optimize, @@ -309,8 +277,8 @@ def main(args): init_steps = 0 if args.resume_from_lora_checkpoint: - transformer, optimizer, init_steps = resume_lora_optimizer( - transformer, args.resume_from_lora_checkpoint, optimizer) + transformer, optimizer, init_steps = resume_lora_optimizer(transformer, args.resume_from_lora_checkpoint, + optimizer) main_print(f"optimizer: {optimizer}") lr_scheduler = get_scheduler( @@ -323,8 +291,7 @@ def main(args): last_epoch=init_steps - 1, ) - train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, - args.cfg) + train_dataset = LatentDataset(args.data_json_path, args.num_latent_t, args.cfg) sampler = (LengthGroupedSampler( args.train_batch_size, rank=rank, @@ -346,42 +313,33 @@ def main(args): ) num_update_steps_per_epoch = math.ceil( - len(train_dataloader) / args.gradient_accumulation_steps * - args.sp_size / args.train_sp_batch_size) - args.num_train_epochs = math.ceil(args.max_train_steps / - num_update_steps_per_epoch) + len(train_dataloader) / args.gradient_accumulation_steps * args.sp_size / args.train_sp_batch_size) + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) if rank <= 0: project = args.tracker_project_name or "fastvideo" wandb.init(project=project, config=args) # Train! - total_batch_size = (world_size * args.gradient_accumulation_steps / - args.sp_size * args.train_sp_batch_size) + total_batch_size = (world_size * args.gradient_accumulation_steps / args.sp_size * args.train_sp_batch_size) main_print("***** Running training *****") main_print(f" Num examples = {len(train_dataset)}") main_print(f" Dataloader size = {len(train_dataloader)}") main_print(f" Num Epochs = {args.num_train_epochs}") main_print(f" Resume training from step {init_steps}") - main_print( - f" Instantaneous batch size per device = {args.train_batch_size}") - main_print( - f" Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}" - ) - main_print( - f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + main_print(f" Instantaneous batch size per device = {args.train_batch_size}") + main_print(f" Total train batch size (w. data & sequence parallel, accumulation) = {total_batch_size}") + main_print(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") main_print(f" Total optimization steps = {args.max_train_steps}") main_print( f" Total training parameters per FSDP shard = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e9} B" ) # print dtype - main_print( - f" Master weight dtype: {transformer.parameters().__next__().dtype}") + main_print(f" Master weight dtype: {transformer.parameters().__next__().dtype}") # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: - assert NotImplementedError( - "resume_from_checkpoint is not supported now.") + assert NotImplementedError("resume_from_checkpoint is not supported now.") # TODO progress_bar = tqdm( @@ -449,26 +407,18 @@ def main(args): if step % args.checkpointing_steps == 0: if args.use_lora: # Save LoRA weights - save_lora_checkpoint(transformer, optimizer, rank, - args.output_dir, step, pipe) + save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, step, pipe) else: # Your existing checkpoint saving code save_checkpoint(transformer, rank, args.output_dir, step) dist.barrier() if args.log_validation and step % args.validation_steps == 0: - log_validation(args, - transformer, - device, - torch.bfloat16, - step, - shift=args.shift) + log_validation(args, transformer, device, torch.bfloat16, step, shift=args.shift) if args.use_lora: - save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, - args.max_train_steps, pipe) + save_lora_checkpoint(transformer, optimizer, rank, args.output_dir, args.max_train_steps, pipe) else: - save_checkpoint(transformer, rank, args.output_dir, - args.max_train_steps) + save_checkpoint(transformer, rank, args.output_dir, args.max_train_steps) if get_sequence_parallel_state(): destroy_sequence_parallel_group() @@ -476,13 +426,10 @@ def main(args): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - "--model_type", - type=str, - default="mochi", - help= - "The type of model to train. Currentlt support [mochi, hunyuan_hf, hunyuan]" - ) + parser.add_argument("--model_type", + type=str, + default="mochi", + help="The type of model to train. Currentlt support [mochi, hunyuan_hf, hunyuan]") # dataset & dataloader parser.add_argument("--data_json_path", type=str, required=True) parser.add_argument("--num_height", type=int, default=480) @@ -492,8 +439,7 @@ if __name__ == "__main__": "--dataloader_num_workers", type=int, default=10, - help= - "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--train_batch_size", @@ -501,10 +447,7 @@ if __name__ == "__main__": default=16, help="Batch size (per device) for the training dataloader.", ) - parser.add_argument("--num_latent_t", - type=int, - default=28, - help="Number of latent timesteps.") + parser.add_argument("--num_latent_t", type=int, default=28, help="Number of latent timesteps.") parser.add_argument("--group_frame", action="store_true") # TODO parser.add_argument("--group_resolution", action="store_true") # TODO @@ -541,16 +484,12 @@ if __name__ == "__main__": parser.add_argument("--validation_steps", type=int, default=50) parser.add_argument("--log_validation", action="store_true") parser.add_argument("--tracker_project_name", type=str, default=None) - parser.add_argument("--seed", - type=int, - default=None, - help="A seed for reproducible training.") + parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--output_dir", type=str, default=None, - help= - "The output directory where the model predictions and checkpoints will be written.", + help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--checkpoints_total_limit", @@ -562,40 +501,31 @@ if __name__ == "__main__": "--checkpointing_steps", type=int, default=500, - help= - ("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" - " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" - " training using `--resume_from_checkpoint`."), + help=("Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" + " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" + " training using `--resume_from_checkpoint`."), ) - parser.add_argument("--shift", - type=float, - default=1.0, - help=("Set shift to 7 for hunyuan model.")) + parser.add_argument("--shift", type=float, default=1.0, help=("Set shift to 7 for hunyuan model.")) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, - help= - ("Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), + help=("Whether training should be resumed from a previous checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'), ) parser.add_argument( "--resume_from_lora_checkpoint", type=str, default=None, - help= - ("Whether training should be resumed from a previous lora checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), + help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'), ) parser.add_argument( "--logging_dir", type=str, default="logs", - help= - ("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), + help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), ) # optimizer & scheduler & Training @@ -604,29 +534,25 @@ if __name__ == "__main__": "--max_train_steps", type=int, default=None, - help= - "Total number of training steps to perform. If provided, overrides num_train_epochs.", + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, - help= - "Number of updates steps to accumulate before performing a backward/update pass.", + help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, - help= - "Initial learning rate (after the potential warmup period) to use.", + help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, - help= - "Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_warmup_steps", @@ -634,47 +560,36 @@ if __name__ == "__main__": default=10, help="Number of steps for the warmup in the lr scheduler.", ) - parser.add_argument("--max_grad_norm", - default=1.0, - type=float, - help="Max gradient norm.") + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--gradient_checkpointing", action="store_true", - help= - "Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument("--selective_checkpointing", type=float, default=1.0) parser.add_argument( "--allow_tf32", action="store_true", - help= - ("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" - " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" - ), + help=("Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" + " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], - help= - ("Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), + help=( + "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" + " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."), ) parser.add_argument( "--use_cpu_offload", action="store_true", - help= - "Whether to use CPU offload for param & gradient & optimizer states.", + help="Whether to use CPU offload for param & gradient & optimizer states.", ) - parser.add_argument("--sp_size", - type=int, - default=1, - help="For sequence parallel") + parser.add_argument("--sp_size", type=int, default=1, help="For sequence parallel") parser.add_argument( "--train_sp_batch_size", type=int, @@ -688,14 +603,8 @@ if __name__ == "__main__": default=False, help="Whether to use LoRA for finetuning.", ) - parser.add_argument("--lora_alpha", - type=int, - default=256, - help="Alpha parameter for LoRA.") - parser.add_argument("--lora_rank", - type=int, - default=128, - help="LoRA rank parameter. ") + parser.add_argument("--lora_alpha", type=int, default=256, help="Alpha parameter for LoRA.") + parser.add_argument("--lora_rank", type=int, default=128, help="LoRA rank parameter. ") parser.add_argument("--fsdp_sharding_startegy", default="full") parser.add_argument( @@ -720,17 +629,15 @@ if __name__ == "__main__": "--mode_scale", type=float, default=1.29, - help= - "Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.", + help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.", ) # lr_scheduler parser.add_argument( "--lr_scheduler", type=str, default="constant", - help= - ('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]'), + help=('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]'), ) parser.add_argument( "--lr_num_cycles", @@ -744,10 +651,7 @@ if __name__ == "__main__": default=1.0, help="Power factor of the polynomial scheduler.", ) - parser.add_argument("--weight_decay", - type=float, - default=0.01, - help="Weight decay to apply.") + parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay to apply.") parser.add_argument( "--master_weight_type", type=str, diff --git a/fastvideo/utils/checkpoint.py b/fastvideo/utils/checkpoint.py index 162c68a9e..385cde633 100644 --- a/fastvideo/utils/checkpoint.py +++ b/fastvideo/utils/checkpoint.py @@ -6,24 +6,17 @@ import torch import torch.distributed.checkpoint as dist_cp from peft import get_peft_model_state_dict from safetensors.torch import load_file, save_file -from torch.distributed.checkpoint.default_planner import (DefaultLoadPlanner, - DefaultSavePlanner) +from torch.distributed.checkpoint.default_planner import (DefaultLoadPlanner, DefaultSavePlanner) from torch.distributed.checkpoint.optimizer import \ load_sharded_optimizer_state_dict -from torch.distributed.fsdp import (FullOptimStateDictConfig, - FullStateDictConfig) +from torch.distributed.fsdp import (FullOptimStateDictConfig, FullStateDictConfig) from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import StateDictType from fastvideo.utils.logging_ import main_print -def save_checkpoint_optimizer(model, - optimizer, - rank, - output_dir, - step, - discriminator=False): +def save_checkpoint_optimizer(model, optimizer, rank, output_dir, step, discriminator=False): with FSDP.state_dict_type( model, StateDictType.FULL_STATE_DICT, @@ -41,8 +34,7 @@ def save_checkpoint_optimizer(model, os.makedirs(save_dir, exist_ok=True) # save using safetensors if rank <= 0 and not discriminator: - weight_path = os.path.join(save_dir, - "diffusion_pytorch_model.safetensors") + weight_path = os.path.join(save_dir, "diffusion_pytorch_model.safetensors") save_file(cpu_state, weight_path) config_dict = dict(model.config) config_dict.pop('dtype') @@ -53,8 +45,7 @@ def save_checkpoint_optimizer(model, optimizer_path = os.path.join(save_dir, "optimizer.pt") torch.save(optim_state, optimizer_path) else: - weight_path = os.path.join(save_dir, - "discriminator_pytorch_model.safetensors") + weight_path = os.path.join(save_dir, "discriminator_pytorch_model.safetensors") save_file(cpu_state, weight_path) optimizer_path = os.path.join(save_dir, "discriminator_optimizer.pt") torch.save(optim_state, optimizer_path) @@ -74,8 +65,7 @@ def save_checkpoint(transformer, rank, output_dir, step): save_dir = os.path.join(output_dir, f"checkpoint-{step}") os.makedirs(save_dir, exist_ok=True) # save using safetensors - weight_path = os.path.join(save_dir, - "diffusion_pytorch_model.safetensors") + weight_path = os.path.join(save_dir, "diffusion_pytorch_model.safetensors") save_file(cpu_state, weight_path) config_dict = dict(transformer.config) if "dtype" in config_dict: @@ -115,8 +105,7 @@ def save_checkpoint_generator_discriminator( # save dict as json with open(config_path, "w") as f: json.dump(config_dict, f, indent=4) - weight_path = os.path.join(hf_weight_dir, - "diffusion_pytorch_model.safetensors") + weight_path = os.path.join(hf_weight_dir, "diffusion_pytorch_model.safetensors") save_file(cpu_state, weight_path) main_print(f"--> saved HF weight checkpoint at path {hf_weight_dir}") @@ -140,8 +129,7 @@ def save_checkpoint_generator_discriminator( planner=DefaultSavePlanner(), ) - discriminator_fsdp_state_dir = os.path.join(save_dir, - "discriminator_fsdp_state") + discriminator_fsdp_state_dir = os.path.join(save_dir, "discriminator_fsdp_state") os.makedirs(discriminator_fsdp_state_dir, exist_ok=True) with FSDP.state_dict_type( discriminator, @@ -149,13 +137,11 @@ def save_checkpoint_generator_discriminator( FullStateDictConfig(offload_to_cpu=True, rank0_only=True), FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True), ): - optim_state = FSDP.optim_state_dict(discriminator, - discriminator_optimizer) + optim_state = FSDP.optim_state_dict(discriminator, discriminator_optimizer) model_state = discriminator.state_dict() state_dict = {"optimizer": optim_state, "model": model_state} if rank <= 0: - discriminator_fsdp_state_fil = os.path.join( - discriminator_fsdp_state_dir, "discriminator_state.pt") + discriminator_fsdp_state_fil = os.path.join(discriminator_fsdp_state_dir, "discriminator_state.pt") torch.save(state_dict, discriminator_fsdp_state_fil) main_print("--> saved FSDP state checkpoint") @@ -171,8 +157,7 @@ def load_sharded_model(model, optimizer, model_dir, optimizer_dir): storage_reader=dist_cp.FileSystemReader(optimizer_dir), ) optim_state = optim_state["optimizer"] - flattened_osd = FSDP.optim_state_dict_to_load( - model=model, optim=optimizer, optim_state_dict=optim_state) + flattened_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=optim_state) optimizer.load_state_dict(flattened_osd) dist_cp.load_state_dict( state_dict=weight_state_dict, @@ -199,37 +184,30 @@ def load_full_state_model(model, optimizer, checkpoint_file, rank): else: optim_state = None model.load_state_dict(model_state) - discriminator_optim_state = FSDP.optim_state_dict_to_load( - model=model, optim=optimizer, optim_state_dict=optim_state) + discriminator_optim_state = FSDP.optim_state_dict_to_load(model=model, + optim=optimizer, + optim_state_dict=optim_state) optimizer.load_state_dict(discriminator_optim_state) - main_print( - f"--> loaded discriminator and discriminator optimizer from path {checkpoint_file}" - ) + main_print(f"--> loaded discriminator and discriminator optimizer from path {checkpoint_file}") return model, optimizer -def resume_training_generator_discriminator(model, optimizer, discriminator, - discriminator_optimizer, - checkpoint_dir, rank): +def resume_training_generator_discriminator(model, optimizer, discriminator, discriminator_optimizer, checkpoint_dir, + rank): step = int(checkpoint_dir.split("-")[-1]) model_weight_dir = os.path.join(checkpoint_dir, "model_weights_state") model_optimizer_dir = os.path.join(checkpoint_dir, "model_optimizer_state") - model, optimizer = load_sharded_model(model, optimizer, model_weight_dir, - model_optimizer_dir) - discriminator_ckpt_file = os.path.join(checkpoint_dir, - "discriminator_fsdp_state", - "discriminator_state.pt") - discriminator, discriminator_optimizer = load_full_state_model( - discriminator, discriminator_optimizer, discriminator_ckpt_file, rank) + model, optimizer = load_sharded_model(model, optimizer, model_weight_dir, model_optimizer_dir) + discriminator_ckpt_file = os.path.join(checkpoint_dir, "discriminator_fsdp_state", "discriminator_state.pt") + discriminator, discriminator_optimizer = load_full_state_model(discriminator, discriminator_optimizer, + discriminator_ckpt_file, rank) return model, optimizer, discriminator, discriminator_optimizer, step def resume_training(model, optimizer, checkpoint_dir, discriminator=False): - weight_path = os.path.join(checkpoint_dir, - "diffusion_pytorch_model.safetensors") + weight_path = os.path.join(checkpoint_dir, "diffusion_pytorch_model.safetensors") if discriminator: - weight_path = os.path.join(checkpoint_dir, - "discriminator_pytorch_model.safetensors") + weight_path = os.path.join(checkpoint_dir, "discriminator_pytorch_model.safetensors") model_weights = load_file(weight_path) with FSDP.state_dict_type( @@ -246,15 +224,13 @@ def resume_training(model, optimizer, checkpoint_dir, discriminator=False): else: optim_path = os.path.join(checkpoint_dir, "optimizer.pt") optimizer_state_dict = torch.load(optim_path, weights_only=False) - optim_state = FSDP.optim_state_dict_to_load( - model=model, optim=optimizer, optim_state_dict=optimizer_state_dict) + optim_state = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=optimizer_state_dict) optimizer.load_state_dict(optim_state) step = int(checkpoint_dir.split("-")[-1]) return model, optimizer, step -def save_lora_checkpoint(transformer, optimizer, rank, output_dir, step, - pipeline): +def save_lora_checkpoint(transformer, optimizer, rank, output_dir, step, pipeline): with FSDP.state_dict_type( transformer, StateDictType.FULL_STATE_DICT, @@ -275,8 +251,7 @@ def save_lora_checkpoint(transformer, optimizer, rank, output_dir, step, torch.save(lora_optim_state, optim_path) # save lora weight main_print(f"--> saving LoRA checkpoint at step {step}") - transformer_lora_layers = get_peft_model_state_dict( - model=transformer, state_dict=full_state_dict) + transformer_lora_layers = get_peft_model_state_dict(model=transformer, state_dict=full_state_dict) pipeline.save_lora_weights( save_directory=save_dir, transformer_lora_layers=transformer_lora_layers, @@ -303,10 +278,9 @@ def resume_lora_optimizer(transformer, checkpoint_dir, optimizer): config_dict = json.load(f) optim_path = os.path.join(checkpoint_dir, "lora_optimizer.pt") optimizer_state_dict = torch.load(optim_path, weights_only=False) - optim_state = FSDP.optim_state_dict_to_load( - model=transformer, - optim=optimizer, - optim_state_dict=optimizer_state_dict) + optim_state = FSDP.optim_state_dict_to_load(model=transformer, + optim=optimizer, + optim_state_dict=optimizer_state_dict) optimizer.load_state_dict(optim_state) step = config_dict["step"] main_print(f"--> Successfully resuming LoRA optimizer from step {step}") diff --git a/fastvideo/utils/communications.py b/fastvideo/utils/communications.py index 6d18be611..26a7a07c9 100644 --- a/fastvideo/utils/communications.py +++ b/fastvideo/utils/communications.py @@ -17,10 +17,7 @@ def broadcast(input_: torch.Tensor): dist.broadcast(input_, src=src, group=nccl_info.group) -def _all_to_all_4D(input: torch.tensor, - scatter_idx: int = 2, - gather_idx: int = 1, - group=None) -> torch.tensor: +def _all_to_all_4D(input: torch.tensor, scatter_idx: int = 2, gather_idx: int = 1, group=None) -> torch.tensor: """ all-to-all for QKV @@ -33,9 +30,7 @@ def _all_to_all_4D(input: torch.tensor, Returns: torch.tensor: resharded tensor (bs, seqlen/P, hc, hs) """ - assert ( - input.dim() == 4 - ), f"input must be 4D tensor, got {input.dim()} and shape {input.shape}" + assert (input.dim() == 4), f"input must be 4D tensor, got {input.dim()} and shape {input.shape}" seq_world_size = dist.get_world_size(group) @@ -47,8 +42,7 @@ def _all_to_all_4D(input: torch.tensor, # transpose groups of heads with the seq-len parallel dimension, so that we can scatter them! # (bs, seqlen/P, hc, hs) -reshape-> (bs, seq_len/P, P, hc/P, hs) -transpose(0,2)-> (P, seq_len/P, bs, hc/P, hs) - input_t = (input.reshape(bs, shard_seqlen, seq_world_size, shard_hc, - hs).transpose(0, 2).contiguous()) + input_t = (input.reshape(bs, shard_seqlen, seq_world_size, shard_hc, hs).transpose(0, 2).contiguous()) output = torch.empty_like(input_t) # https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_to_all_single @@ -62,8 +56,7 @@ def _all_to_all_4D(input: torch.tensor, output = output.reshape(seqlen, bs, shard_hc, hs) # (seq_len, bs, hc/P, hs) -reshape-> (bs, seq_len, hc/P, hs) - output = output.transpose(0, 1).contiguous().reshape( - bs, seqlen, shard_hc, hs) + output = output.transpose(0, 1).contiguous().reshape(bs, seqlen, shard_hc, hs) return output @@ -76,10 +69,11 @@ def _all_to_all_4D(input: torch.tensor, # transpose groups of heads with the seq-len parallel dimension, so that we can scatter them! # (bs, seqlen, hc/P, hs) -reshape-> (bs, P, seq_len/P, hc/P, hs) -transpose(0, 3)-> (hc/P, P, seqlen/P, bs, hs) -transpose(0, 1) -> (P, hc/P, seqlen/P, bs, hs) - input_t = (input.reshape( - bs, seq_world_size, shard_seqlen, shard_hc, - hs).transpose(0, 3).transpose(0, 1).contiguous().reshape( - seq_world_size, shard_hc, shard_seqlen, bs, hs)) + input_t = (input.reshape(bs, seq_world_size, shard_seqlen, shard_hc, + hs).transpose(0, + 3).transpose(0, + 1).contiguous().reshape(seq_world_size, shard_hc, + shard_seqlen, bs, hs)) output = torch.empty_like(input_t) # https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_to_all_single @@ -94,13 +88,11 @@ def _all_to_all_4D(input: torch.tensor, output = output.reshape(hc, shard_seqlen, bs, hs) # (hc, seqlen/N, bs, hs) -tranpose(0,2)-> (bs, seqlen/N, hc, hs) - output = output.transpose(0, 2).contiguous().reshape( - bs, shard_seqlen, hc, hs) + output = output.transpose(0, 2).contiguous().reshape(bs, shard_seqlen, hc, hs) return output else: - raise RuntimeError( - "scatter_idx must be 1 or 2 and gather_idx must be 1 or 2") + raise RuntimeError("scatter_idx must be 1 or 2 and gather_idx must be 1 or 2") class SeqAllToAll4D(torch.autograd.Function): @@ -120,12 +112,10 @@ class SeqAllToAll4D(torch.autograd.Function): return _all_to_all_4D(input, scatter_idx, gather_idx, group=group) @staticmethod - def backward(ctx: Any, - *grad_output: Tensor) -> Tuple[None, Tensor, None, None]: + def backward(ctx: Any, *grad_output: Tensor) -> Tuple[None, Tensor, None, None]: return ( None, - SeqAllToAll4D.apply(ctx.group, *grad_output, ctx.gather_idx, - ctx.scatter_idx), + SeqAllToAll4D.apply(ctx.group, *grad_output, ctx.gather_idx, ctx.scatter_idx), None, None, ) @@ -136,8 +126,7 @@ def all_to_all_4D( scatter_dim: int = 2, gather_dim: int = 1, ): - return SeqAllToAll4D.apply(nccl_info.group, input_, scatter_dim, - gather_dim) + return SeqAllToAll4D.apply(nccl_info.group, input_, scatter_dim, gather_dim) def _all_to_all( @@ -147,10 +136,7 @@ def _all_to_all( scatter_dim: int, gather_dim: int, ): - input_list = [ - t.contiguous() - for t in torch.tensor_split(input_, world_size, scatter_dim) - ] + input_list = [t.contiguous() for t in torch.tensor_split(input_, world_size, scatter_dim)] output_list = [torch.empty_like(input_list[0]) for _ in range(world_size)] dist.all_to_all(output_list, input_list, group=group) return torch.cat(output_list, dim=gather_dim).contiguous() @@ -172,8 +158,7 @@ class _AllToAll(torch.autograd.Function): ctx.scatter_dim = scatter_dim ctx.gather_dim = gather_dim ctx.world_size = dist.get_world_size(process_group) - output = _all_to_all(input_, ctx.world_size, process_group, - scatter_dim, gather_dim) + output = _all_to_all(input_, ctx.world_size, process_group, scatter_dim, gather_dim) return output @staticmethod @@ -253,8 +238,7 @@ def all_gather(input_: torch.Tensor, dim: int = 1): return _AllGather.apply(input_, dim) -def prepare_sequence_parallel_data(hidden_states, encoder_hidden_states, - attention_mask, encoder_attention_mask): +def prepare_sequence_parallel_data(hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask): if nccl_info.sp_size == 1: return ( hidden_states, @@ -263,18 +247,11 @@ def prepare_sequence_parallel_data(hidden_states, encoder_hidden_states, encoder_attention_mask, ) - def prepare(hidden_states, encoder_hidden_states, attention_mask, - encoder_attention_mask): + def prepare(hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask): hidden_states = all_to_all(hidden_states, scatter_dim=2, gather_dim=0) - encoder_hidden_states = all_to_all(encoder_hidden_states, - scatter_dim=1, - gather_dim=0) - attention_mask = all_to_all(attention_mask, - scatter_dim=1, - gather_dim=0) - encoder_attention_mask = all_to_all(encoder_attention_mask, - scatter_dim=1, - gather_dim=0) + encoder_hidden_states = all_to_all(encoder_hidden_states, scatter_dim=1, gather_dim=0) + attention_mask = all_to_all(attention_mask, scatter_dim=1, gather_dim=0) + encoder_attention_mask = all_to_all(encoder_attention_mask, scatter_dim=1, gather_dim=0) return ( hidden_states, encoder_hidden_states, @@ -301,8 +278,7 @@ def prepare_sequence_parallel_data(hidden_states, encoder_hidden_states, return hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask -def sp_parallel_dataloader_wrapper(dataloader, device, train_batch_size, - sp_size, train_sp_batch_size): +def sp_parallel_dataloader_wrapper(dataloader, device, train_batch_size, sp_size, train_sp_batch_size): while True: for data_item in dataloader: latents, cond, attn_mask, cond_mask = data_item @@ -316,11 +292,9 @@ def sp_parallel_dataloader_wrapper(dataloader, device, train_batch_size, else: latents, cond, attn_mask, cond_mask = prepare_sequence_parallel_data( latents, cond, attn_mask, cond_mask) - assert ( - train_batch_size * sp_size >= train_sp_batch_size - ), "train_batch_size * sp_size should be greater than train_sp_batch_size" - for iter in range(train_batch_size * sp_size // - train_sp_batch_size): + assert (train_batch_size * sp_size >= + train_sp_batch_size), "train_batch_size * sp_size should be greater than train_sp_batch_size" + for iter in range(train_batch_size * sp_size // train_sp_batch_size): st_idx = iter * train_sp_batch_size ed_idx = (iter + 1) * train_sp_batch_size encoder_hidden_states = cond[st_idx:ed_idx] diff --git a/fastvideo/utils/dataset_utils.py b/fastvideo/utils/dataset_utils.py index 40ac9b53a..3c8c9e5a6 100644 --- a/fastvideo/utils/dataset_utils.py +++ b/fastvideo/utils/dataset_utils.py @@ -30,9 +30,7 @@ class DecordInit(object): results (dict): The resulting dict to be modified and passed to the next transform in pipeline. """ - reader = decord.VideoReader(filename, - ctx=self.ctx, - num_threads=self.num_threads) + reader = decord.VideoReader(filename, ctx=self.ctx, num_threads=self.num_threads) return reader def __repr__(self): @@ -94,8 +92,7 @@ class Collate: self.max_thw, self.ae_stride_thw, ) - assert not torch.any( - torch.isnan(pad_batch_tubes)), "after pad_batch_tubes" + assert not torch.any(torch.isnan(pad_batch_tubes)), "after pad_batch_tubes" return pad_batch_tubes, attention_mask, input_ids, cond_mask def process( @@ -109,25 +106,18 @@ class Collate: ae_stride_thw, ): # pad to max multiple of ds_stride - batch_input_size = [i.shape - for i in batch_tubes] # [(c t h w), (c t h w)] + batch_input_size = [i.shape for i in batch_tubes] # [(c t h w), (c t h w)] assert len(batch_input_size) == self.batch_size if self.group_frame or self.group_resolution or self.batch_size == 1: # len_each_batch = batch_input_size - idx_length_dict = dict( - [*zip(list(range(self.batch_size)), len_each_batch)]) + idx_length_dict = dict([*zip(list(range(self.batch_size)), len_each_batch)]) count_dict = Counter(len_each_batch) if len(count_dict) != 1: - sorted_by_value = sorted(count_dict.items(), - key=lambda item: item[1]) + sorted_by_value = sorted(count_dict.items(), key=lambda item: item[1]) pick_length = sorted_by_value[-1][0] # the highest frequency - candidate_batch = [ - idx for idx, length in idx_length_dict.items() - if length == pick_length - ] + candidate_batch = [idx for idx, length in idx_length_dict.items() if length == pick_length] random_select_batch = [ - random.choice(candidate_batch) - for _ in range(len(len_each_batch) - len(candidate_batch)) + random.choice(candidate_batch) for _ in range(len(len_each_batch) - len(candidate_batch)) ] print( batch_input_size, @@ -141,8 +131,7 @@ class Collate: pick_idx = candidate_batch + random_select_batch batch_tubes = [batch_tubes[i] for i in pick_idx] - batch_input_size = [i.shape for i in batch_tubes - ] # [(c t h w), (c t h w)] + batch_input_size = [i.shape for i in batch_tubes] # [(c t h w), (c t h w)] input_ids = [input_ids[i] for i in pick_idx] # b [1, l] cond_mask = [cond_mask[i] for i in pick_idx] # b [1, l] @@ -159,10 +148,7 @@ class Collate: pad_to_multiple(max_w, ds_stride), ) pad_max_t = pad_max_t + 1 - self.ae_stride_t - each_pad_t_h_w = [[ - pad_max_t - i.shape[1], pad_max_h - i.shape[2], - pad_max_w - i.shape[3] - ] for i in batch_tubes] + each_pad_t_h_w = [[pad_max_t - i.shape[1], pad_max_h - i.shape[2], pad_max_w - i.shape[3]] for i in batch_tubes] pad_batch_tubes = [ F.pad(im, (0, pad_w, 0, pad_h, 0, pad_t), value=0) for (pad_t, pad_h, pad_w), im in zip(each_pad_t_h_w, batch_tubes) @@ -229,10 +215,7 @@ def split_to_even_chunks(indices, lengths, num_chunks, batch_size): if batch_size != len(chunk): assert batch_size > len(chunk) if len(chunk) != 0: - chunk = chunk + [ - random.choice(chunk) - for _ in range(batch_size - len(chunk)) - ] + chunk = chunk + [random.choice(chunk) for _ in range(batch_size - len(chunk))] else: chunk = random.choice(pad_chunks) print(chunks[idx], "->", chunk) @@ -256,16 +239,11 @@ def megabatch_frame_alignment(megabatches, lengths): # mixed frame length, align megabatch inside if len(count_dict) != 1: - sorted_by_value = sorted(count_dict.items(), - key=lambda item: item[1]) + sorted_by_value = sorted(count_dict.items(), key=lambda item: item[1]) pick_length = sorted_by_value[-1][0] # the highest frequency - candidate_batch = [ - idx for idx, length in idx_length_dict.items() - if length == pick_length - ] + candidate_batch = [idx for idx, length in idx_length_dict.items() if length == pick_length] random_select_batch = [ - random.choice(candidate_batch) - for i in range(len(idx_length_dict) - len(candidate_batch)) + random.choice(candidate_batch) for i in range(len(idx_length_dict) - len(candidate_batch)) ] aligned_magabatch = candidate_batch + random_select_batch aligned_magabatches.append(aligned_magabatch) @@ -287,8 +265,7 @@ def get_length_grouped_indices( ): # We need to use torch for the random part as a distributed sampler will set the random seed for torch. if generator is None: - generator = torch.Generator().manual_seed( - seed) # every rank will generate a fixed order but random index + generator = torch.Generator().manual_seed(seed) # every rank will generate a fixed order but random index indices = torch.randperm(len(lengths), generator=generator).tolist() @@ -297,29 +274,20 @@ def get_length_grouped_indices( # chunk dataset to megabatches megabatch_size = world_size * batch_size - megabatches = [ - indices[i:i + megabatch_size] - for i in range(0, len(lengths), megabatch_size) - ] + megabatches = [indices[i:i + megabatch_size] for i in range(0, len(lengths), megabatch_size)] # make sure the length in each magabatch is align with each other megabatches = megabatch_frame_alignment(megabatches, lengths) # aplit aligned megabatch into batches - megabatches = [ - split_to_even_chunks(megabatch, lengths, world_size, batch_size) - for megabatch in megabatches - ] + megabatches = [split_to_even_chunks(megabatch, lengths, world_size, batch_size) for megabatch in megabatches] # random megabatches to do video-image mix training indices = torch.randperm(len(megabatches), generator=generator).tolist() shuffled_megabatches = [megabatches[i] for i in indices] # expand indices and return - return [ - i for megabatch in shuffled_megabatches for batch in megabatch - for i in batch - ] + return [i for megabatch in shuffled_megabatches for batch in megabatch for i in batch] class LengthGroupedSampler(Sampler): @@ -370,6 +338,5 @@ class LengthGroupedSampler(Sampler): index += batch_size * world_size return result - indices = distributed_sampler(indices, self.rank, self.batch_size, - self.world_size) + indices = distributed_sampler(indices, self.rank, self.batch_size, self.world_size) return iter(indices) diff --git a/fastvideo/utils/env_utils.py b/fastvideo/utils/env_utils.py index 206c4aa1b..13591e9aa 100644 --- a/fastvideo/utils/env_utils.py +++ b/fastvideo/utils/env_utils.py @@ -35,6 +35,4 @@ if __name__ == "__main__": except Exception: pass - print("\n" + - "\n".join([f"- {key}: {value}" - for key, value in info.items()]) + "\n") + print("\n" + "\n".join([f"- {key}: {value}" for key, value in info.items()]) + "\n") diff --git a/fastvideo/utils/fsdp_util.py b/fastvideo/utils/fsdp_util.py index cadf33a66..929edbacb 100644 --- a/fastvideo/utils/fsdp_util.py +++ b/fastvideo/utils/fsdp_util.py @@ -4,8 +4,8 @@ from functools import partial import torch from peft.utils.other import fsdp_auto_wrap_policy -from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( - CheckpointImpl, apply_activation_checkpointing, checkpoint_wrapper) +from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (CheckpointImpl, apply_activation_checkpointing, + checkpoint_wrapper) from torch.distributed.fsdp import MixedPrecision, ShardingStrategy from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy @@ -93,8 +93,7 @@ def get_dit_fsdp_kwargs( sharding_strategy = ShardingStrategy._HYBRID_SHARD_ZERO2 device_id = torch.cuda.current_device() - cpu_offload = (torch.distributed.fsdp.CPUOffload( - offload_params=True) if cpu_offload else None) + cpu_offload = (torch.distributed.fsdp.CPUOffload(offload_params=True) if cpu_offload else None) fsdp_kwargs = { "auto_wrap_policy": auto_wrap_policy, "mixed_precision": mixed_precision, diff --git a/fastvideo/utils/load.py b/fastvideo/utils/load.py index fe748cf99..df5873992 100644 --- a/fastvideo/utils/load.py +++ b/fastvideo/utils/load.py @@ -7,16 +7,14 @@ from diffusers import AutoencoderKLHunyuanVideo, AutoencoderKLMochi from torch import nn from transformers import AutoTokenizer, T5EncoderModel -from fastvideo.models.hunyuan.modules.models import ( - HYVideoDiffusionTransformer, MMDoubleStreamBlock, MMSingleStreamBlock) +from fastvideo.models.hunyuan.modules.models import (HYVideoDiffusionTransformer, MMDoubleStreamBlock, + MMSingleStreamBlock) from fastvideo.models.hunyuan.text_encoder import TextEncoder from fastvideo.models.hunyuan.vae.autoencoder_kl_causal_3d import \ AutoencoderKLCausal3D -from fastvideo.models.hunyuan_hf.modeling_hunyuan import ( - HunyuanVideoSingleTransformerBlock, HunyuanVideoTransformer3DModel, - HunyuanVideoTransformerBlock) -from fastvideo.models.mochi_hf.modeling_mochi import (MochiTransformer3DModel, - MochiTransformerBlock) +from fastvideo.models.hunyuan_hf.modeling_hunyuan import (HunyuanVideoSingleTransformerBlock, + HunyuanVideoTransformer3DModel, HunyuanVideoTransformerBlock) +from fastvideo.models.mochi_hf.modeling_mochi import (MochiTransformer3DModel, MochiTransformerBlock) from fastvideo.utils.logging_ import main_print hunyuan_config = { @@ -62,8 +60,7 @@ class HunyuanTextEncoderWrapper(nn.Module): super().__init__() text_len = 256 - crop_start = PROMPT_TEMPLATE["dit-llm-encode-video"].get( - "crop_start", 0) + crop_start = PROMPT_TEMPLATE["dit-llm-encode-video"].get("crop_start", 0) max_length = text_len + crop_start @@ -72,8 +69,7 @@ class HunyuanTextEncoderWrapper(nn.Module): # prompt_template_video prompt_template_video = PROMPT_TEMPLATE["dit-llm-encode-video"] - text_encoder_path = os.path.join(pretrained_model_name_or_path, - "text_encoder") + text_encoder_path = os.path.join(pretrained_model_name_or_path, "text_encoder") self.text_encoder = TextEncoder( text_encoder_type="llm", text_encoder_path=text_encoder_path, @@ -88,8 +84,7 @@ class HunyuanTextEncoderWrapper(nn.Module): logger=None, device=device, ) - text_encoder_path_2 = os.path.join(pretrained_model_name_or_path, - "text_encoder_2") + text_encoder_path_2 = os.path.join(pretrained_model_name_or_path, "text_encoder_2") self.text_encoder_2 = TextEncoder( text_encoder_type="clipL", text_encoder_path=text_encoder_path_2, @@ -110,9 +105,7 @@ class HunyuanTextEncoderWrapper(nn.Module): text_inputs = text_encoder.text2tokens(prompt, data_type=data_type) if clip_skip is None: - prompt_outputs = text_encoder.encode(text_inputs, - data_type="video", - device=device) + prompt_outputs = text_encoder.encode(text_inputs, data_type="video", device=device) prompt_embeds = prompt_outputs.hidden_state else: prompt_outputs = text_encoder.encode( @@ -123,16 +116,14 @@ class HunyuanTextEncoderWrapper(nn.Module): ) prompt_embeds = prompt_outputs.hidden_states_list[-(clip_skip + 1)] - prompt_embeds = text_encoder.model.text_model.final_layer_norm( - prompt_embeds) + prompt_embeds = text_encoder.model.text_model.final_layer_norm(prompt_embeds) attention_mask = prompt_outputs.attention_mask if attention_mask is not None: attention_mask = attention_mask.to(device) bs_embed, seq_len = attention_mask.shape attention_mask = attention_mask.repeat(1, num_videos_per_prompt) - attention_mask = attention_mask.view( - bs_embed * num_videos_per_prompt, seq_len) + attention_mask = attention_mask.view(bs_embed * num_videos_per_prompt, seq_len) if text_encoder is not None: prompt_embeds_dtype = text_encoder.dtype @@ -141,27 +132,23 @@ class HunyuanTextEncoderWrapper(nn.Module): else: prompt_embeds_dtype = prompt_embeds.dtype - prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, - device=device) + prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) if prompt_embeds.ndim == 2: bs_embed, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt) - prompt_embeds = prompt_embeds.view( - bs_embed * num_videos_per_prompt, -1) + prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, -1) else: bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) - prompt_embeds = prompt_embeds.view( - bs_embed * num_videos_per_prompt, seq_len, -1) + prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, seq_len, -1) return (prompt_embeds, attention_mask) def encode_prompt(self, prompt): prompt_embeds, attention_mask = self.encode_(prompt, self.text_encoder) - prompt_embeds_2, attention_mask_2 = self.encode_( - prompt, self.text_encoder_2) + prompt_embeds_2, attention_mask_2 = self.encode_(prompt, self.text_encoder_2) prompt_embeds_2 = F.pad( prompt_embeds_2, (0, prompt_embeds.shape[2] - prompt_embeds_2.shape[1]), @@ -175,11 +162,9 @@ class MochiTextEncoderWrapper(nn.Module): def __init__(self, pretrained_model_name_or_path, device): super().__init__() - self.text_encoder = T5EncoderModel.from_pretrained( - os.path.join(pretrained_model_name_or_path, - "text_encoder")).to(device) - self.tokenizer = AutoTokenizer.from_pretrained( - os.path.join(pretrained_model_name_or_path, "tokenizer")) + self.text_encoder = T5EncoderModel.from_pretrained(os.path.join(pretrained_model_name_or_path, + "text_encoder")).to(device) + self.tokenizer = AutoTokenizer.from_pretrained(os.path.join(pretrained_model_name_or_path, "tokenizer")) self.max_sequence_length = 256 def encode_prompt(self, prompt): @@ -201,19 +186,12 @@ class MochiTextEncoderWrapper(nn.Module): prompt_attention_mask = text_inputs.attention_mask prompt_attention_mask = prompt_attention_mask.bool().to(device) - untruncated_ids = self.tokenizer(prompt, - padding="longest", - return_tensors="pt").input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - if untruncated_ids.shape[-1] >= text_input_ids.shape[ - -1] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer.batch_decode( - untruncated_ids[:, self.max_sequence_length - 1:-1]) - main_print( - f"Truncated text input: {prompt} to: {removed_text} for model input." - ) - prompt_embeds = self.text_encoder( - text_input_ids.to(device), attention_mask=prompt_attention_mask)[0] + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.max_sequence_length - 1:-1]) + main_print(f"Truncated text input: {prompt} to: {removed_text} for model input.") + prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) # duplicate text embeddings for each generation per prompt, using mps friendly method @@ -229,20 +207,16 @@ def load_hunyuan_state_dict(model, dit_model_name_or_path): model_path = dit_model_name_or_path bare_model = "unknown" - state_dict = torch.load(model_path, - map_location=lambda storage, loc: storage, - weights_only=True) + state_dict = torch.load(model_path, map_location=lambda storage, loc: storage, weights_only=True) - if bare_model == "unknown" and ("ema" in state_dict - or "module" in state_dict): + if bare_model == "unknown" and ("ema" in state_dict or "module" in state_dict): bare_model = False if bare_model is False: if load_key in state_dict: state_dict = state_dict[load_key] else: - raise KeyError( - f"Missing key: `{load_key}` in the checkpoint: {model_path}. The keys in the checkpoint " - f"are: {list(state_dict.keys())}.") + raise KeyError(f"Missing key: `{load_key}` in the checkpoint: {model_path}. The keys in the checkpoint " + f"are: {list(state_dict.keys())}.") model.load_state_dict(state_dict, strict=True) return model @@ -288,8 +262,7 @@ def load_transformer( **hunyuan_config, dtype=master_weight_type, ) - transformer = load_hunyuan_state_dict(transformer, - dit_model_name_or_path) + transformer = load_hunyuan_state_dict(transformer, dit_model_name_or_path) if master_weight_type == torch.bfloat16: transformer = transformer.bfloat16() else: @@ -300,23 +273,20 @@ def load_transformer( def load_vae(model_type, pretrained_model_name_or_path): weight_dtype = torch.float32 if model_type == "mochi": - vae = AutoencoderKLMochi.from_pretrained( - pretrained_model_name_or_path, - subfolder="vae", - torch_dtype=weight_dtype).to("cuda") + vae = AutoencoderKLMochi.from_pretrained(pretrained_model_name_or_path, + subfolder="vae", + torch_dtype=weight_dtype).to("cuda") autocast_type = torch.bfloat16 fps = 30 elif model_type == "hunyuan_hf": - vae = AutoencoderKLHunyuanVideo.from_pretrained( - pretrained_model_name_or_path, - subfolder="vae", - torch_dtype=weight_dtype).to("cuda") + vae = AutoencoderKLHunyuanVideo.from_pretrained(pretrained_model_name_or_path, + subfolder="vae", + torch_dtype=weight_dtype).to("cuda") autocast_type = torch.bfloat16 fps = 24 elif model_type == "hunyuan": vae_precision = torch.float32 - vae_path = os.path.join(pretrained_model_name_or_path, - "hunyuan-video-t2v-720p/vae") + vae_path = os.path.join(pretrained_model_name_or_path, "hunyuan-video-t2v-720p/vae") config = AutoencoderKLCausal3D.load_config(vae_path) vae = AutoencoderKLCausal3D.from_config(config) @@ -328,10 +298,7 @@ def load_vae(model_type, pretrained_model_name_or_path): if "state_dict" in ckpt: ckpt = ckpt["state_dict"] if any(k.startswith("vae.") for k in ckpt.keys()): - ckpt = { - k.replace("vae.", ""): v - for k, v in ckpt.items() if k.startswith("vae.") - } + ckpt = {k.replace("vae.", ""): v for k, v in ckpt.items() if k.startswith("vae.")} vae.load_state_dict(ckpt) vae = vae.to(dtype=vae_precision) vae.requires_grad_(False) @@ -344,11 +311,9 @@ def load_vae(model_type, pretrained_model_name_or_path): def load_text_encoder(model_type, pretrained_model_name_or_path, device): if model_type == "mochi": - text_encoder = MochiTextEncoderWrapper(pretrained_model_name_or_path, - device) + text_encoder = MochiTextEncoderWrapper(pretrained_model_name_or_path, device) elif model_type == "hunyuan" or "hunyuan_hf": - text_encoder = HunyuanTextEncoderWrapper(pretrained_model_name_or_path, - device) + text_encoder = HunyuanTextEncoderWrapper(pretrained_model_name_or_path, device) else: raise ValueError(f"Unsupported model type: {model_type}") return text_encoder @@ -359,8 +324,7 @@ def get_no_split_modules(transformer): if isinstance(transformer, MochiTransformer3DModel): return (MochiTransformerBlock, ) elif isinstance(transformer, HunyuanVideoTransformer3DModel): - return (HunyuanVideoSingleTransformerBlock, - HunyuanVideoTransformerBlock) + return (HunyuanVideoSingleTransformerBlock, HunyuanVideoTransformerBlock) elif isinstance(transformer, HYVideoDiffusionTransformer): return (MMDoubleStreamBlock, MMSingleStreamBlock) else: @@ -371,7 +335,6 @@ if __name__ == "__main__": # test encode prompt device = torch.cuda.current_device() pretrained_model_name_or_path = "data/hunyuan" - text_encoder = load_text_encoder("hunyuan", pretrained_model_name_or_path, - device) + text_encoder = load_text_encoder("hunyuan", pretrained_model_name_or_path, device) prompt = "A man on stage claps his hands together while facing the audience. The audience, visible in the foreground, holds up mobile devices to record the event, capturing the moment from various angles. The background features a large banner with text identifying the man on stage. Throughout the sequence, the man's expression remains engaged and directed towards the audience. The camera angle remains constant, focusing on capturing the interaction between the man on stage and the audience." prompt_embeds, attention_mask = text_encoder.encode_prompt(prompt) diff --git a/fastvideo/utils/optimizer.py b/fastvideo/utils/optimizer.py index ffee08a8e..661f30379 100644 --- a/fastvideo/utils/optimizer.py +++ b/fastvideo/utils/optimizer.py @@ -13,23 +13,18 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False): ) args.optimizer = "adamw" - if args.use_8bit_adam and not (args.optimizer.lower() - not in ["adam", "adamw"]): - logger.warning( - f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was " - f"set to {args.optimizer.lower()}") + if args.use_8bit_adam and not (args.optimizer.lower() not in ["adam", "adamw"]): + logger.warning(f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was " + f"set to {args.optimizer.lower()}") if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: - raise ImportError( - "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." - ) + raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.") if args.optimizer.lower() == "adamw": - optimizer_class = (bnb.optim.AdamW8bit - if args.use_8bit_adam else torch.optim.AdamW) + optimizer_class = (bnb.optim.AdamW8bit if args.use_8bit_adam else torch.optim.AdamW) optimizer = optimizer_class( params_to_optimize, @@ -50,16 +45,13 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False): try: import prodigyopt except ImportError: - raise ImportError( - "To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`" - ) + raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`") optimizer_class = prodigyopt.Prodigy if args.learning_rate <= 0.1: logger.warning( - "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0" - ) + "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0") optimizer = optimizer_class( params_to_optimize, diff --git a/fastvideo/utils/parallel_states.py b/fastvideo/utils/parallel_states.py index 552a9dccf..c1f4cd751 100644 --- a/fastvideo/utils/parallel_states.py +++ b/fastvideo/utils/parallel_states.py @@ -50,8 +50,7 @@ def initialize_sequence_parallel_group(sequence_parallel_size): nccl_info.global_rank = rank num_sequence_parallel_groups: int = world_size // sequence_parallel_size for i in range(num_sequence_parallel_groups): - ranks = range(i * sequence_parallel_size, - (i + 1) * sequence_parallel_size) + ranks = range(i * sequence_parallel_size, (i + 1) * sequence_parallel_size) group = dist.new_group(ranks) if rank in ranks: nccl_info.group = group diff --git a/fastvideo/utils/validation.py b/fastvideo/utils/validation.py index 562518189..dc4e3cafd 100644 --- a/fastvideo/utils/validation.py +++ b/fastvideo/utils/validation.py @@ -14,12 +14,10 @@ from tqdm import tqdm import wandb from fastvideo.distill.solver import PCMFMScheduler -from fastvideo.models.mochi_hf.pipeline_mochi import ( - linear_quadratic_schedule, retrieve_timesteps) +from fastvideo.models.mochi_hf.pipeline_mochi import (linear_quadratic_schedule, retrieve_timesteps) from fastvideo.utils.communications import all_gather from fastvideo.utils.load import load_vae -from fastvideo.utils.parallel_states import (get_sequence_parallel_state, - nccl_info) +from fastvideo.utils.parallel_states import (get_sequence_parallel_state, nccl_info) def prepare_latents( @@ -40,10 +38,7 @@ def prepare_latents( shape = (batch_size, num_channels_latents, num_frames, height, width) - latents = randn_tensor(shape, - generator=generator, - device=device, - dtype=dtype) + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) return latents @@ -76,10 +71,8 @@ def sample_validation_video( do_classifier_free_guidance = guidance_scale > 1.0 if do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], - dim=0) - prompt_attention_mask = torch.cat( - [negative_prompt_attention_mask, prompt_attention_mask], dim=0) + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) # 4. Prepare latent variables # TODO: Remove hardcore @@ -97,9 +90,7 @@ def sample_validation_video( ) world_size, rank = nccl_info.sp_size, nccl_info.rank_within_group if get_sequence_parallel_state(): - latents = rearrange(latents, - "b t (n s) h w -> b t n s h w", - n=world_size).contiguous() + latents = rearrange(latents, "b t (n s) h w -> b t n s h w", n=world_size).contiguous() latents = latents[:, :, rank, :, :, :] # 5. Prepare timestep @@ -121,8 +112,7 @@ def sample_validation_video( num_inference_steps, device, ) - num_warmup_steps = max( - len(timesteps) - num_inference_steps * scheduler.order, 0) + num_warmup_steps = max(len(timesteps) - num_inference_steps * scheduler.order, 0) # 6. Denoising loop # with self.progress_bar(total=num_inference_steps) as progress_bar: @@ -135,8 +125,7 @@ def sample_validation_video( desc="Validation sampling...", ) as progress_bar: for i, t in enumerate(timesteps): - latent_model_input = (torch.cat([latents] * 2) - if do_classifier_free_guidance else latents) + latent_model_input = (torch.cat([latents] * 2) if do_classifier_free_guidance else latents) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) with torch.autocast("cuda", dtype=torch.bfloat16): @@ -152,15 +141,11 @@ def sample_validation_video( noise_pred = noise_pred.to(torch.float32) if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * ( - noise_pred_text - noise_pred_uncond) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype - latents = scheduler.step(noise_pred, - t, - latents.to(torch.float32), - return_dict=False)[0] + latents = scheduler.step(noise_pred, t, latents.to(torch.float32), return_dict=False)[0] latents = latents.to(latents_dtype) if latents.dtype != latents_dtype: @@ -168,8 +153,7 @@ def sample_validation_video( # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 latents = latents.to(latents_dtype) - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and - (i + 1) % scheduler.order == 0): + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0): progress_bar.update() if get_sequence_parallel_state(): @@ -180,24 +164,19 @@ def sample_validation_video( else: # unscale/denormalize the latents # denormalize with the mean and std if available and not None - has_latents_mean = (hasattr(vae.config, "latents_mean") - and vae.config.latents_mean is not None) - has_latents_std = (hasattr(vae.config, "latents_std") - and vae.config.latents_std is not None) + has_latents_mean = (hasattr(vae.config, "latents_mean") and vae.config.latents_mean is not None) + has_latents_std = (hasattr(vae.config, "latents_std") and vae.config.latents_std is not None) if has_latents_mean and has_latents_std: - latents_mean = (torch.tensor(vae.config.latents_mean).view( - 1, 12, 1, 1, 1).to(latents.device, latents.dtype)) - latents_std = (torch.tensor(vae.config.latents_std).view( - 1, 12, 1, 1, 1).to(latents.device, latents.dtype)) + latents_mean = (torch.tensor(vae.config.latents_mean).view(1, 12, 1, 1, + 1).to(latents.device, latents.dtype)) + latents_std = (torch.tensor(vae.config.latents_std).view(1, 12, 1, 1, 1).to(latents.device, latents.dtype)) latents = latents * latents_std / vae.config.scaling_factor + latents_mean else: latents = latents / vae.config.scaling_factor with torch.autocast("cuda", dtype=vae.dtype): video = vae.decode(latents, return_dict=False)[0] - video_processor = VideoProcessor( - vae_scale_factor=vae_spatial_scale_factor) - video = video_processor.postprocess_video(video, - output_type=output_type) + video_processor = VideoProcessor(vae_scale_factor=vae_spatial_scale_factor) + video = video_processor.postprocess_video(video, output_type=output_type) return (video, ) @@ -229,8 +208,7 @@ def log_validation( num_channels_latents = 16 else: raise ValueError(f"Model type {args.model_type} not supported") - vae, autocast_type, fps = load_vae(args.model_type, - args.pretrained_model_name_or_path) + vae, autocast_type, fps = load_vae(args.model_type, args.pretrained_model_name_or_path) vae.enable_tiling() if scheduler_type == "euler": scheduler = FlowMatchEulerDiscreteScheduler(shift=shift) @@ -247,9 +225,7 @@ def log_validation( # args.validation_prompt_dir validation_guidance_scale_ls = args.validation_guidance_scale.split(",") - validation_guidance_scale_ls = [ - float(scale) for scale in validation_guidance_scale_ls - ] + validation_guidance_scale_ls = [float(scale) for scale in validation_guidance_scale_ls] for validation_sampling_step in args.validation_sampling_steps.split(","): validation_sampling_step = int(validation_sampling_step) for validation_guidance_scale in validation_guidance_scale_ls: @@ -257,37 +233,29 @@ def log_validation( # prompt_embed are named embed0 to embedN # check how many embeds are there embe_dir = os.path.join(args.validation_prompt_dir, "prompt_embed") - mask_dir = os.path.join(args.validation_prompt_dir, - "prompt_attention_mask") + mask_dir = os.path.join(args.validation_prompt_dir, "prompt_attention_mask") embeds = sorted([f for f in os.listdir(embe_dir)]) masks = sorted([f for f in os.listdir(mask_dir)]) num_embeds = len(embeds) validation_prompt_ids = list(range(num_embeds)) - num_sp_groups = int(os.getenv("WORLD_SIZE", - "1")) // nccl_info.sp_size + num_sp_groups = int(os.getenv("WORLD_SIZE", "1")) // nccl_info.sp_size # pad to multiple of groups if num_embeds % num_sp_groups != 0: - validation_prompt_ids += [0] * (num_sp_groups - - num_embeds % num_sp_groups) + validation_prompt_ids += [0] * (num_sp_groups - num_embeds % num_sp_groups) num_embeds_per_group = len(validation_prompt_ids) // num_sp_groups local_prompt_ids = validation_prompt_ids[nccl_info.group_id * - num_embeds_per_group: - (nccl_info.group_id + 1) * + num_embeds_per_group:(nccl_info.group_id + 1) * num_embeds_per_group] for i in local_prompt_ids: prompt_embed_path = os.path.join(embe_dir, f"{embeds[i]}") prompt_mask_path = os.path.join(mask_dir, f"{masks[i]}") - prompt_embeds = (torch.load( - prompt_embed_path, map_location="cpu", - weights_only=True).to(device).unsqueeze(0)) - prompt_attention_mask = (torch.load( - prompt_mask_path, map_location="cpu", - weights_only=True).to(device).unsqueeze(0)) - negative_prompt_embeds = torch.zeros( - 256, 4096).to(device).unsqueeze(0) - negative_prompt_attention_mask = ( - torch.zeros(256).bool().to(device).unsqueeze(0)) + prompt_embeds = (torch.load(prompt_embed_path, map_location="cpu", + weights_only=True).to(device).unsqueeze(0)) + prompt_attention_mask = (torch.load(prompt_mask_path, map_location="cpu", + weights_only=True).to(device).unsqueeze(0)) + negative_prompt_embeds = torch.zeros(256, 4096).to(device).unsqueeze(0) + negative_prompt_attention_mask = (torch.zeros(256).bool().to(device).unsqueeze(0)) generator = torch.Generator(device="cpu").manual_seed(12345) video = sample_validation_video( args.model_type, @@ -304,8 +272,7 @@ def log_validation( prompt_embeds=prompt_embeds, prompt_attention_mask=prompt_attention_mask, negative_prompt_embeds=negative_prompt_embeds, - negative_prompt_attention_mask= - negative_prompt_attention_mask, + negative_prompt_attention_mask=negative_prompt_attention_mask, vae_spatial_scale_factor=vae_spatial_scale_factor, vae_temporal_scale_factor=vae_temporal_scale_factor, num_channels_latents=num_channels_latents, @@ -318,9 +285,7 @@ def log_validation( torch.cuda.empty_cache() # log if main process torch.distributed.barrier() - all_videos = [ - None for i in range(int(os.getenv("WORLD_SIZE", "1"))) - ] # remove padded videos + all_videos = [None for i in range(int(os.getenv("WORLD_SIZE", "1")))] # remove padded videos torch.distributed.all_gather_object(all_videos, videos) if nccl_info.global_rank == 0: # remove padding @@ -338,9 +303,6 @@ def log_validation( logs = { f"{'ema_' if ema else ''}validation_sample_{validation_sampling_step}_guidance_{validation_guidance_scale}": - [ - wandb.Video(filename) - for i, filename in enumerate(video_filenames) - ] + [wandb.Video(filename) for i, filename in enumerate(video_filenames)] } wandb.log(logs, step=global_step) diff --git a/format.sh b/format.sh index 520d74b29..60c8b508a 100644 --- a/format.sh +++ b/format.sh @@ -115,7 +115,9 @@ CODESPELL_EXCLUDES=( fastvideo/distill.py, fastvideo/models/hunyuan/modules/models.py, fastvideo/models/mochi_hf/modeling_mochi.py, - fastvideo/utils/env_utils.py' + fastvideo/utils/env_utils.py, + ./csrc/sliding_tile_attention/tk/**' + ) # check spelling of specified files @@ -144,7 +146,7 @@ spell_check_changed() { # Run Codespell ## This flag runs spell check of individual files. --files *must* be the first command line -## arg to use this option. +# arg to use this option. if [[ "$1" == '--files' ]]; then spell_check "${@:2}" # If `--all` is passed, then any further arguments are ignored and the @@ -196,13 +198,17 @@ else fi echo 'FastVideo ruff: Done' +ISORT_EXCLUDES=( + '--skip' './csrc/sliding_tile_attention/tk' +) + # check spelling of specified files isort_check() { isort "$@" } isort_check_all(){ - isort . + isort "${ISORT_EXCLUDES[@]}" . } # Spelling check of files that differ from main branch. diff --git a/predict.py b/predict.py index 2e310e56b..7a1d088e1 100644 --- a/predict.py +++ b/predict.py @@ -38,8 +38,7 @@ class Predictor(BasePredictor): if not os.path.exists(MODEL_CACHE): download_weights(MODEL_URL, MODEL_CACHE) - self.device = torch.device( - "cuda" if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") args = argparse.Namespace( num_frames=125, height=720, @@ -56,8 +55,7 @@ class Predictor(BasePredictor): num_videos=1, load_key='module', use_cpu_offload=False, - dit_weight= - 'FastHunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt', + dit_weight='FastHunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt', reproduce=True, disable_autocast=False, flow_reverse=True, @@ -87,50 +85,26 @@ class Predictor(BasePredictor): text_len_2=77, model_path=MODEL_CACHE, ) - self.model = HunyuanVideoSampler.from_pretrained(MODEL_CACHE, - args=args) + self.model = HunyuanVideoSampler.from_pretrained(MODEL_CACHE, args=args) def predict( - self, - prompt: str = Input( - description="Text prompt for video generation", - default="A cat walks on the grass, realistic style."), - negative_prompt: str = Input( - description= - "Text prompt to specify what you don't want in the video.", - default=""), - width: int = Input(description="Width of output video", - default=1280, - ge=256), - height: int = Input(description="Height of output video", - default=720, - ge=256), - num_frames: int = Input(description="Number of frames to generate", - default=125, - ge=16), - num_inference_steps: int = Input( - description="Number of denoising steps", default=6, ge=1, le=50), - guidance_scale: float = Input( - description="Classifier free guidance scale", - default=1.0, - ge=0.1, - le=10.0), - embedded_cfg_scale: float = Input( - description="Embedded classifier free guidance scale", - default=6.0, - ge=0.1, - le=10.0), - flow_shift: int = Input(description="Flow shift parameter", - default=17, - ge=1, - le=20), - fps: int = Input(description="Frames per second of output video", - default=24, - ge=1, - le=60), - seed: int = Input( - description="0 for Random seed. Set for reproducible generation", - default=0), + self, + prompt: str = Input(description="Text prompt for video generation", + default="A cat walks on the grass, realistic style."), + negative_prompt: str = Input(description="Text prompt to specify what you don't want in the video.", + default=""), + width: int = Input(description="Width of output video", default=1280, ge=256), + height: int = Input(description="Height of output video", default=720, ge=256), + num_frames: int = Input(description="Number of frames to generate", default=125, ge=16), + num_inference_steps: int = Input(description="Number of denoising steps", default=6, ge=1, le=50), + guidance_scale: float = Input(description="Classifier free guidance scale", default=1.0, ge=0.1, le=10.0), + embedded_cfg_scale: float = Input(description="Embedded classifier free guidance scale", + default=6.0, + ge=0.1, + le=10.0), + flow_shift: int = Input(description="Flow shift parameter", default=17, ge=1, le=20), + fps: int = Input(description="Frames per second of output video", default=24, ge=1, le=60), + seed: int = Input(description="0 for Random seed. Set for reproducible generation", default=0), ) -> Path: """Run video generation""" if seed <= 0: diff --git a/pyproject.toml b/pyproject.toml index 3428d3fb1..2433c955d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,16 +12,35 @@ classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: Apache Software License", ] + dependencies = [ -"transformers==4.46.1", "accelerate==1.0.1", "tokenizers==0.20.1", "albumentations==1.4.20", "av==13.1.0", -"decord==0.6.0", "einops==0.8.0", "fastapi==0.115.3", "gdown==5.2.0", "h5py==3.12.1", "idna==3.6", "imageio==2.36.0", -"matplotlib==3.9.2", "numpy==1.26.3", "omegaconf==2.3.0", "opencv-python==4.10.0.84", "opencv-python-headless==4.10.0.84", -"pandas==2.2.3", "pillow==10.2.0", "pydub==0.25.1", "pytorch-lightning==2.4.0", "pytorchvideo==0.1.5", "PyYAML==6.0.1", -"regex==2024.9.11", "requests==2.31.0", "scikit-learn==1.5.2", "scipy==1.14.1", "six==1.16.0", "test-tube==0.7.5", -"timm==1.0.11", "torchdiffeq==0.2.4", "torchmetrics==1.5.1", "tqdm==4.66.5", "urllib3==2.2.0", "uvicorn==0.32.0", -"scikit-video==1.1.11", "imageio-ffmpeg==0.5.1", "sentencepiece==0.2.0", "beautifulsoup4==4.12.3", "ftfy==6.3.0", -"moviepy==1.0.3", "wandb==0.18.5", "tensorboard==2.18.0", "pydantic==2.9.2", "gradio==5.3.0", "huggingface_hub==0.26.1", "protobuf==5.28.3", -"watch", "gpustat", "peft==0.13.2", "liger_kernel==0.4.1", "einops==0.8.0", "wheel==0.44.0", "loguru", "diffusers==0.32.0", "bitsandbytes"] + # Core Libraries + "scipy==1.14.1", "six==1.16.0", "h5py==3.12.1", + + # Machine Learning & Transformers + "transformers==4.46.1", "tokenizers==0.20.1", "sentencepiece==0.2.0", + "timm==1.0.11", "peft==0.13.2", "diffusers==0.32.0", "bitsandbytes", + + # Acceleration & Optimization + "accelerate==1.0.1", "torch", + + # Computer Vision & Image Processing + "opencv-python==4.10.0.84", "pillow==10.2.0", "imageio==2.36.0", + "imageio-ffmpeg==0.5.1", "decord==0.6.0", "einops", + + # Experiment Tracking & Logging + "wandb==0.18.5", "loguru", "test-tube==0.7.5", + + # Miscellaneous Utilities + "tqdm==4.66.5", "PyYAML==6.0.1", "idna==3.6", "protobuf==5.28.3", + "gradio==5.3.0", "huggingface_hub==0.26.1", "moviepy==1.0.3", "flask", "flask_restful", "aiohttp", + + # System & Monitoring Tools + "gpustat", "watch", + + # Kernel & Packaging + "wheel" +] [tool.setuptools.packages.find] @@ -39,7 +58,16 @@ check_untyped_defs = true no_implicit_optional = true [tool.codespell] -skip ="./data,./wandb,csrc/sliding_tile_attention/tk" +skip ="./data,./wandb,./csrc/sliding_tile_attention/tk" [tool.ruff.lint.per-file-ignores] +"fastvideo/models/stepvideo/diffusion/video_pipeline.py" = ["F821"] +"fastvideo/sample/call_remote_server_stepvideo.py" = ["E722"] +"csrc/sliding_tile_attention/test/bench.py" = ["F841"] +"fastvideo/models/stepvideo/__init__.py" = ["F403"] +"fastvideo/models/stepvideo/utils/__init__.py" = ["F403"] # Ignore all files that end in `_test.py`. -"fastvideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py" = ["E741"] \ No newline at end of file +"fastvideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py" = ["E741"] + + +[tool.yapf] +column_limit = 120 \ No newline at end of file diff --git a/scripts/dataset_preparation/prepare_json_file.py b/scripts/dataset_preparation/prepare_json_file.py index 3a9ca8d0a..91b7e663f 100644 --- a/scripts/dataset_preparation/prepare_json_file.py +++ b/scripts/dataset_preparation/prepare_json_file.py @@ -57,9 +57,7 @@ def process_videos_and_prompts(video_dir_path, prompt_dir_path, verbose=False): # Ensure directories exist if not video_dir.exists() or not prompt_dir.exists(): - print( - f"Error: One or both directories do not exist:\nVideos: {video_dir}\nPrompts: {prompt_dir}" - ) + print(f"Error: One or both directories do not exist:\nVideos: {video_dir}\nPrompts: {prompt_dir}") return [] # Process each video file @@ -107,26 +105,14 @@ def parse_args(): """Parse command line arguments""" import argparse - parser = argparse.ArgumentParser( - description='Process videos and their corresponding prompt files') - parser.add_argument('--video_dir', - '-v', + parser = argparse.ArgumentParser(description='Process videos and their corresponding prompt files') + parser.add_argument('--video_dir', '-v', required=True, help='Directory containing video files') + parser.add_argument('--prompt_dir', '-p', required=True, help='Directory containing prompt text files') + parser.add_argument('--output_path', + '-o', required=True, - help='Directory containing video files') - parser.add_argument('--prompt_dir', - '-p', - required=True, - help='Directory containing prompt text files') - parser.add_argument( - '--output_path', - '-o', - required=True, - help= - 'Full path for output JSON file (e.g., /path/to/output/videos2caption.json)' - ) - parser.add_argument('--verbose', - action='store_true', - help='Print verbose processing information') + help='Full path for output JSON file (e.g., /path/to/output/videos2caption.json)') + parser.add_argument('--verbose', action='store_true', help='Print verbose processing information') return parser.parse_args() @@ -136,9 +122,7 @@ if __name__ == "__main__": args = parse_args() # Process videos and prompts - processed_videos = process_videos_and_prompts(args.video_dir, - args.prompt_dir, - args.verbose) + processed_videos = process_videos_and_prompts(args.video_dir, args.prompt_dir, args.verbose) if processed_videos: # Save results diff --git a/scripts/dataset_preparation/resize_videos.py b/scripts/dataset_preparation/resize_videos.py index 523fa6ef2..5f5e22406 100644 --- a/scripts/dataset_preparation/resize_videos.py +++ b/scripts/dataset_preparation/resize_videos.py @@ -41,10 +41,7 @@ def resize_video(args_tuple): def process_frame(frame): frame_float = frame.astype(float) / 255.0 - resized = resize(frame_float, (height, width, 3), - mode='reflect', - anti_aliasing=True, - preserve_range=True) + resized = resize(frame_float, (height, width, 3), mode='reflect', anti_aliasing=True, preserve_range=True) return (resized * 255).astype(np.uint8) resized = video.fl_image(process_frame) @@ -68,10 +65,7 @@ def process_folder(args): output_path.mkdir(parents=True, exist_ok=True) video_extensions = {'.mp4', '.avi', '.mov', '.mkv', '.webm'} - video_files = [ - f for f in input_path.iterdir() - if f.is_file() and f.suffix.lower() in video_extensions - ] + video_files = [f for f in input_path.iterdir() if f.is_file() and f.suffix.lower() in video_extensions] if not video_files: print(f"No video files found in {args.input_dir}") @@ -81,25 +75,19 @@ def process_folder(args): print(f"Target: {args.width}x{args.height} at {args.fps}fps") # Prepare arguments for parallel processing - process_args = [(video_file, output_path, args.width, args.height, - args.fps) for video_file in video_files] + process_args = [(video_file, output_path, args.width, args.height, args.fps) for video_file in video_files] successful = 0 skipped = 0 failed = [] # Use ProcessPoolExecutor instead of ThreadPoolExecutor - with tqdm(total=len(video_files), - desc="Converting videos", - dynamic_ncols=True) as pbar: + with tqdm(total=len(video_files), desc="Converting videos", dynamic_ncols=True) as pbar: # Use max_workers as specified or default to CPU count max_workers = args.max_workers with ProcessPoolExecutor(max_workers=max_workers) as executor: # Submit all tasks - future_to_file = { - executor.submit(resize_video, arg): arg[0] - for arg in process_args - } + future_to_file = {executor.submit(resize_video, arg): arg[0] for arg in process_args} # Process completed tasks for future in as_completed(future_to_file): @@ -113,9 +101,7 @@ def process_folder(args): pbar.update(1) # Print final summary - print( - f"\nDone! Processed: {successful}, Skipped: {skipped}, Failed: {len(failed)}" - ) + print(f"\nDone! Processed: {successful}, Skipped: {skipped}, Failed: {len(failed)}") if failed: print("Failed files:") for fname, error in failed: @@ -123,32 +109,16 @@ def process_folder(args): def parse_args(): - parser = argparse.ArgumentParser( - description= - 'Batch resize videos to specified resolution and FPS (16:9 only)') - parser.add_argument('--input_dir', - required=True, - help='Input directory containing video files') - parser.add_argument('--output_dir', - required=True, - help='Output directory for processed videos') - parser.add_argument('--width', + parser = argparse.ArgumentParser(description='Batch resize videos to specified resolution and FPS (16:9 only)') + parser.add_argument('--input_dir', required=True, help='Input directory containing video files') + parser.add_argument('--output_dir', required=True, help='Output directory for processed videos') + parser.add_argument('--width', type=int, default=1280, help='Target width in pixels (default: 848)') + parser.add_argument('--height', type=int, default=720, help='Target height in pixels (default: 480)') + parser.add_argument('--fps', type=int, default=30, help='Target frames per second (default: 30)') + parser.add_argument('--max_workers', type=int, - default=1280, - help='Target width in pixels (default: 848)') - parser.add_argument('--height', - type=int, - default=720, - help='Target height in pixels (default: 480)') - parser.add_argument('--fps', - type=int, - default=30, - help='Target frames per second (default: 30)') - parser.add_argument( - '--max_workers', - type=int, - default=4, - help='Maximum number of concurrent processes (default: 4)') + default=4, + help='Maximum number of concurrent processes (default: 4)') parser.add_argument('--log-level', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'], default='INFO', diff --git a/scripts/huggingface/download_hf.py b/scripts/huggingface/download_hf.py index fd26ee77d..f3c37ac5d 100644 --- a/scripts/huggingface/download_hf.py +++ b/scripts/huggingface/download_hf.py @@ -5,11 +5,8 @@ from huggingface_hub import hf_hub_download, snapshot_download # set args for repo_id, local_dir, repo_type, if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Download a dataset or model from the Hugging Face Hub") - parser.add_argument("--repo_id", - type=str, - help="The ID of the repository to download") + parser = argparse.ArgumentParser(description="Download a dataset or model from the Hugging Face Hub") + parser.add_argument("--repo_id", type=str, help="The ID of the repository to download") parser.add_argument( "--local_dir", type=str, @@ -20,9 +17,7 @@ if __name__ == "__main__": type=str, help="The type of repository to download (dataset or model)", ) - parser.add_argument("--file_name", - type=str, - help="The file name to download") + parser.add_argument("--file_name", type=str, help="The file name to download") args = parser.parse_args() if args.file_name: hf_hub_download( diff --git a/scripts/inference/inference_hunyuan_STA.sh b/scripts/inference/inference_hunyuan_STA.sh index bc6793ae1..4fac6bebf 100644 --- a/scripts/inference/inference_hunyuan_STA.sh +++ b/scripts/inference/inference_hunyuan_STA.sh @@ -2,7 +2,7 @@ # Inference with STA + Teacache num_gpus=1 -mask_strategy_file_path=assets/mask_strategy.json +mask_strategy_file_path=assets/mask_strategy_hunyuan.json export MODEL_BASE=data/hunyuan rel_l1_thresh=0.15 CUDA_VISIBLE_DEVICES=1 torchrun --nnodes=1 --nproc_per_node=$num_gpus --master_port 29603 \ diff --git a/scripts/inference/inference_stepvideo.sh b/scripts/inference/inference_stepvideo.sh new file mode 100644 index 000000000..2ce6e64b8 --- /dev/null +++ b/scripts/inference/inference_stepvideo.sh @@ -0,0 +1,19 @@ +#!/bin/bash +# You better have two terminal, one for the remote server, and one for DiT +CUDA_VISIBLE_DEVICES=7 python fastvideo/sample/call_remote_server_stepvideo.py --model_dir data/stepvideo-t2v/ & + +parallel=4 +url='127.0.0.1' +model_dir=data/stepvideo-t2v +torchrun --nproc_per_node $parallel fastvideo/sample/sample_t2v_stepvideo.py \ + --model_dir $model_dir \ + --vae_url $url \ + --caption_url $url \ + --prompt assets/prompt.txt \ + --infer_steps 50 \ + --width 768 \ + --height 768 \ + --num_frames 204 \ + --cfg_scale 9.0 \ + --save_path outputs/ \ + --time_shift 13.0 \ No newline at end of file diff --git a/scripts/inference/inference_stepvideo_STA.sh b/scripts/inference/inference_stepvideo_STA.sh new file mode 100644 index 000000000..b84ce5c34 --- /dev/null +++ b/scripts/inference/inference_stepvideo_STA.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# Inference with STA + Teacache +# You better have two terminal, one for the remote server, and one for DiT +# remove enable_teacache to disable Teacache +CUDA_VISIBLE_DEVICES=7 python fastvideo/sample/call_remote_server_stepvideo.py --model_dir data/stepvideo-t2v/ & +parallel=4 +url='127.0.0.1' +model_dir=data/stepvideo-t2v +mask_strategy_file_path=assets/mask_strategy_stepvideo.json +rel_l1_thresh=0.23 +torchrun --nproc_per_node $parallel fastvideo/sample/sample_t2v_stepvideo_STA.py \ + --model_dir $model_dir \ + --vae_url $url \ + --caption_url $url \ + --prompt "A rocket blasting off from the launch pad, accelerating rapidly into the sky." \ + --infer_steps 50 \ + --width 768 \ + --height 768 \ + --num_frames 204 \ + --cfg_scale 9.0 \ + --save_path outputs/ \ + --time_shift 13.0 \ + --rel_l1_thresh $rel_l1_thresh \ + --enable_teacache \ + --mask_strategy_file_path $mask_strategy_file_path diff --git a/tests/test_data_preprocess.py b/tests/test_data_preprocess.py index f01f16930..a735a8eb1 100644 --- a/tests/test_data_preprocess.py +++ b/tests/test_data_preprocess.py @@ -19,10 +19,8 @@ class TestAutoencoderKLCausal3D(unittest.TestCase): os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1" # Load tokenizer/model that can be reused across all tests - cls.tokenizer = AutoTokenizer.from_pretrained( - "hf-internal-testing/tiny-random-t5") - cls.text_encoder = T5EncoderModel.from_pretrained( - "hf-internal-testing/tiny-random-t5") + cls.tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + cls.text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5") def setUp(self): """ @@ -77,8 +75,7 @@ class TestAutoencoderKLCausal3D(unittest.TestCase): self.model = AutoencoderKLCausal3D(**self.init_dict) # Create a random input tensor - self.input_tensor = torch.rand(self.batch_size, 3, self.init_time_len, - self.init_height, self.init_width) + self.input_tensor = torch.rand(self.batch_size, 3, self.init_time_len, self.init_height, self.init_width) def test_encode_shape(self): """ diff --git a/tests/test_save_checkpoint.py b/tests/test_save_checkpoint.py index 790ff1286..2cf3fca48 100644 --- a/tests/test_save_checkpoint.py +++ b/tests/test_save_checkpoint.py @@ -20,8 +20,7 @@ def setup_distributed(): dist.destroy_process_group() -@pytest.mark.skipif(not torch.cuda.is_available(), - reason="Requires at least 2 GPUs to run NCCL tests") +@pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires at least 2 GPUs to run NCCL tests") def test_save_and_remove_checkpoint(): from fastvideo.models.mochi_hf.modeling_mochi import \ MochiTransformer3DModel diff --git a/tests/test_sequence_parallel.py b/tests/test_sequence_parallel.py index 25c2b959a..151736750 100644 --- a/tests/test_sequence_parallel.py +++ b/tests/test_sequence_parallel.py @@ -6,8 +6,7 @@ import torch import torch.distributed as dist import torch.multiprocessing as mp -from fastvideo.utils.communications import (nccl_info, - prepare_sequence_parallel_data) +from fastvideo.utils.communications import (nccl_info, prepare_sequence_parallel_data) def _init_distributed_test_gpu(rank, world_size, backend, port, data, results): @@ -35,8 +34,7 @@ def _init_distributed_test_gpu(rank, world_size, backend, port, data, results): print(f"Rank {rank} input hidden_states:\n", hidden_states) print(f"Rank {rank} input hidden_states shape:\n", hidden_states.shape) out_hidden, out_encoder, out_attn_mask, out_encoder_mask = prepare_sequence_parallel_data( - hidden_states, encoder_hidden_states, attention_mask, - encoder_attention_mask) + hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask) print(f"Rank {rank} output out_hidden:\n", out_hidden) shapes = ( @@ -45,10 +43,7 @@ def _init_distributed_test_gpu(rank, world_size, backend, port, data, results): out_attn_mask.shape, out_encoder_mask.shape, ) - shape_tensor = torch.tensor( - [*shapes[0], *shapes[1], *shapes[2], *shapes[3]], - dtype=torch.int32, - device=device) + shape_tensor = torch.tensor([*shapes[0], *shapes[1], *shapes[2], *shapes[3]], dtype=torch.int32, device=device) shape_list = [torch.zeros_like(shape_tensor) for _ in range(world_size)] dist.all_gather(shape_list, shape_tensor, group=seq_group) gathered_shapes = [tuple(s.tolist()) for s in shape_list] @@ -63,8 +58,7 @@ def _init_distributed_test_gpu(rank, world_size, backend, port, data, results): dist.destroy_process_group() -@pytest.mark.skipif(not torch.cuda.is_available() - or torch.cuda.device_count() < 2, +@pytest.mark.skipif(not torch.cuda.is_available() or torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs to run NCCL tests") def test_prepare_sequence_parallel_data_gpu(): world_size = 2 @@ -87,8 +81,7 @@ def test_prepare_sequence_parallel_data_gpu(): world_size=world_size, backend=backend, port=port, - data=(hidden_states, encoder_hidden_states, - attention_mask, encoder_attention_mask), + data=(hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask), results=results_dict) # Spawn two GPU processes (rank=0, rank=1) @@ -104,18 +97,14 @@ def test_prepare_sequence_parallel_data_gpu(): if first_rank_shapes is None: first_rank_shapes = rank_shapes assert rank_shapes == first_rank_shapes, ( - f"Mismatch in shapes across ranks: {rank_shapes} != {first_rank_shapes}" - ) + f"Mismatch in shapes across ranks: {rank_shapes} != {first_rank_shapes}") overall_hidden_out.append(rank_data["out_hidden"]) overall_hidden_out = torch.cat(overall_hidden_out, dim=2) print("overall_hidden_out", overall_hidden_out) print("overall_hidden_out_shape", overall_hidden_out.shape) - assert torch.allclose(hidden_states, - torch.tensor(overall_hidden_out), - rtol=1e-7, - atol=1e-6) + assert torch.allclose(hidden_states, torch.tensor(overall_hidden_out), rtol=1e-7, atol=1e-6) if __name__ == "__main__":