feat: Implement self-contained specialist managers

build_apexx.sh

@@ -1,45 +0,0 @@
-#!/bin/bash
-set -e
-
-# 🧾 Config
-# APEX_COMMIT=f3a960f80244cf9e80558ab30f7f7e8cbf03c0a0  # Known stable commit
-
-echo "🧹 Cleaning any previous apex build..."
-rm -rf apex
-rm -rf *.egg-info build dist
-
-echo "📥 Cloning NVIDIA/apex..."
-git clone https://github.com/NVIDIA/apex.git
-cd apex
-# git checkout $APEX_COMMIT
-
-echo "🛠 Installing build dependencies..."
-sudo apt-get update
-sudo apt-get install -y \
-  build-essential \
-  ninja-build \
-  python3-dev \
-  libffi-dev \
-  libncurses5-dev \
-  libncursesw5-dev \
-  libreadline-dev \
-  libssl-dev \
-  libsqlite3-dev \
-  zlib1g-dev \
-  libbz2-dev \
-  liblzma-dev \
-  git
-
-echo "🐍 Upgrading pip and wheel..."
-pip install -U pip setuptools wheel
-
-echo "🧪 Verifying PyTorch + CUDA availability..."
-python -c "import torch; print('PyTorch:', torch.__version__, '| CUDA:', torch.version.cuda)"
-
-echo "🔧 Building Apex with CUDA and C++ extensions..."
-python setup.py bdist_wheel --cuda_ext --cpp_ext
-
-echo "✅ Done! Built wheel:"
-ls -lh dist/*.whl
-
-cd ..

commonx/cache.py

@@ -1,47 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Callable
-
-
-class Cache:
-    """Caching reusable args for faster inference"""
-
-    def __init__(self, disable=False, prefix="", cache=None):
-        self.cache = cache if cache is not None else {}
-        self.disable = disable
-        self.prefix = prefix
-
-    def __call__(self, key: str, fn: Callable):
-        if self.disable:
-            return fn()
-
-        key = self.prefix + key
-        try:
-            result = self.cache[key]
-        except KeyError:
-            result = fn()
-            self.cache[key] = result
-        return result
-
-    def namespace(self, namespace: str):
-        return Cache(
-            disable=self.disable,
-            prefix=self.prefix + namespace + ".",
-            cache=self.cache,
-        )
-
-    def get(self, key: str):
-        key = self.prefix + key
-        return self.cache[key]

commonx/config.py

@@ -1,110 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Configuration utility functions
-"""
-
-import importlib
-from typing import Any, Callable, List, Union
-from omegaconf import DictConfig, ListConfig, OmegaConf
-
-OmegaConf.register_new_resolver("eval", eval)
-
-
-def load_config(path: str, argv: List[str] = None) -> Union[DictConfig, ListConfig]:
-    """
-    Load a configuration. Will resolve inheritance.
-    """
-    config = OmegaConf.load(path)
-    if argv is not None:
-        config_argv = OmegaConf.from_dotlist(argv)
-        config = OmegaConf.merge(config, config_argv)
-    config = resolve_recursive(config, resolve_inheritance)
-    return config
-
-
-def resolve_recursive(
-    config: Any,
-    resolver: Callable[[Union[DictConfig, ListConfig]], Union[DictConfig, ListConfig]],
-) -> Any:
-    config = resolver(config)
-    if isinstance(config, DictConfig):
-        for k in config.keys():
-            v = config.get(k)
-            if isinstance(v, (DictConfig, ListConfig)):
-                config[k] = resolve_recursive(v, resolver)
-    if isinstance(config, ListConfig):
-        for i in range(len(config)):
-            v = config.get(i)
-            if isinstance(v, (DictConfig, ListConfig)):
-                config[i] = resolve_recursive(v, resolver)
-    return config
-
-
-def resolve_inheritance(config: Union[DictConfig, ListConfig]) -> Any:
-    """
-    Recursively resolve inheritance if the config contains:
-    __inherit__: path/to/parent.yaml or a ListConfig of such paths.
-    """
-    if isinstance(config, DictConfig):
-        inherit = config.pop("__inherit__", None)
-
-        if inherit:
-            inherit_list = inherit if isinstance(inherit, ListConfig) else [inherit]
-
-            parent_config = None
-            for parent_path in inherit_list:
-                assert isinstance(parent_path, str)
-                parent_config = (
-                    load_config(parent_path)
-                    if parent_config is None
-                    else OmegaConf.merge(parent_config, load_config(parent_path))
-                )
-
-            if len(config.keys()) > 0:
-                config = OmegaConf.merge(parent_config, config)
-            else:
-                config = parent_config
-    return config
-
-
-def import_item(path: str, name: str) -> Any:
-    """
-    Import a python item. Example: import_item("path.to.file", "MyClass") -> MyClass
-    """
-    return getattr(importlib.import_module(path), name)
-
-
-def create_object(config: DictConfig) -> Any:
-    """
-    Create an object from config.
-    The config is expected to contains the following:
-    __object__:
-      path: path.to.module
-      name: MyClass
-      args: as_config | as_params (default to as_config)
-    """
-    item = import_item(
-        path=config.__object__.path,
-        name=config.__object__.name,
-    )
-    args = config.__object__.get("args", "as_config")
-    if args == "as_config":
-        return item(config)
-    if args == "as_params":
-        config = OmegaConf.to_object(config)
-        config.pop("__object__")
-        return item(**config)
-    raise NotImplementedError(f"Unknown args type: {args}")

commonx/decorators.py

@@ -1,147 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Decorators.
-"""
-
-import functools
-import threading
-import time
-from typing import Callable
-import torch
-
-from common.distributed import barrier_if_distributed, get_global_rank, get_local_rank
-from common.logger import get_logger
-
-logger = get_logger(__name__)
-
-
-def log_on_entry(func: Callable) -> Callable:
-    """
-    Functions with this decorator will log the function name at entry.
-    When using multiple decorators, this must be applied innermost to properly capture the name.
-    """
-
-    def log_on_entry_wrapper(*args, **kwargs):
-        logger.info(f"Entering {func.__name__}")
-        return func(*args, **kwargs)
-
-    return log_on_entry_wrapper
-
-
-def barrier_on_entry(func: Callable) -> Callable:
-    """
-    Functions with this decorator will start executing when all ranks are ready to enter.
-    """
-
-    def barrier_on_entry_wrapper(*args, **kwargs):
-        barrier_if_distributed()
-        return func(*args, **kwargs)
-
-    return barrier_on_entry_wrapper
-
-
-def _conditional_execute_wrapper_factory(execute: bool, func: Callable) -> Callable:
-    """
-    Helper function for local_rank_zero_only and global_rank_zero_only.
-    """
-
-    def conditional_execute_wrapper(*args, **kwargs):
-        # Only execute if needed.
-        result = func(*args, **kwargs) if execute else None
-        # All GPUs must wait.
-        barrier_if_distributed()
-        # Return results.
-        return result
-
-    return conditional_execute_wrapper
-
-
-def _asserted_wrapper_factory(condition: bool, func: Callable, err_msg: str = "") -> Callable:
-    """
-    Helper function for some functions with special constraints,
-    especially functions called by other global_rank_zero_only / local_rank_zero_only ones,
-    in case they are wrongly invoked in other scenarios.
-    """
-
-    def asserted_execute_wrapper(*args, **kwargs):
-        assert condition, err_msg
-        result = func(*args, **kwargs)
-        return result
-
-    return asserted_execute_wrapper
-
-
-def local_rank_zero_only(func: Callable) -> Callable:
-    """
-    Functions with this decorator will only execute on local rank zero.
-    """
-    return _conditional_execute_wrapper_factory(get_local_rank() == 0, func)
-
-
-def global_rank_zero_only(func: Callable) -> Callable:
-    """
-    Functions with this decorator will only execute on global rank zero.
-    """
-    return _conditional_execute_wrapper_factory(get_global_rank() == 0, func)
-
-
-def assert_only_global_rank_zero(func: Callable) -> Callable:
-    """
-    Functions with this decorator are only accessible to processes with global rank zero.
-    """
-    return _asserted_wrapper_factory(
-        get_global_rank() == 0, func, err_msg="Not accessible to processes with global_rank != 0"
-    )
-
-
-def assert_only_local_rank_zero(func: Callable) -> Callable:
-    """
-    Functions with this decorator are only accessible to processes with local rank zero.
-    """
-    return _asserted_wrapper_factory(
-        get_local_rank() == 0, func, err_msg="Not accessible to processes with local_rank != 0"
-    )
-
-
-def new_thread(func: Callable) -> Callable:
-    """
-    Functions with this decorator will run in a new thread.
-    The function will return the thread, which can be joined to wait for completion.
-    """
-
-    def new_thread_wrapper(*args, **kwargs):
-        thread = threading.Thread(target=func, args=args, kwargs=kwargs)
-        thread.start()
-        return thread
-
-    return new_thread_wrapper
-
-
-def log_runtime(func: Callable) -> Callable:
-    """
-    Functions with this decorator will logging the runtime.
-    """
-
-    @functools.wraps(func)
-    def wrapped(*args, **kwargs):
-        torch.distributed.barrier()
-        start = time.perf_counter()
-        result = func(*args, **kwargs)
-        torch.distributed.barrier()
-        logger.info(f"Completed {func.__name__} in {time.perf_counter() - start:.3f} seconds.")
-        return result
-
-    return wrapped

commonx/diffusion/__init__.py

@@ -1,56 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Diffusion package.
-"""
-
-from .config import (
-    create_sampler_from_config,
-    create_sampling_timesteps_from_config,
-    create_schedule_from_config,
-)
-from .samplers.base import Sampler
-from .samplers.euler import EulerSampler
-from .schedules.base import Schedule
-from .schedules.lerp import LinearInterpolationSchedule
-from .timesteps.base import SamplingTimesteps, Timesteps
-from .timesteps.sampling.trailing import UniformTrailingSamplingTimesteps
-from .types import PredictionType, SamplingDirection
-from .utils import classifier_free_guidance, classifier_free_guidance_dispatcher, expand_dims
-
-__all__ = [
-    # Configs
-    "create_sampler_from_config",
-    "create_sampling_timesteps_from_config",
-    "create_schedule_from_config",
-    # Schedules
-    "Schedule",
-    "DiscreteVariancePreservingSchedule",
-    "LinearInterpolationSchedule",
-    # Samplers
-    "Sampler",
-    "EulerSampler",
-    # Timesteps
-    "Timesteps",
-    "SamplingTimesteps",
-    # Types
-    "PredictionType",
-    "SamplingDirection",
-    "UniformTrailingSamplingTimesteps",
-    # Utils
-    "classifier_free_guidance",
-    "classifier_free_guidance_dispatcher",
-    "expand_dims",
-]

commonx/diffusion/config.py

@@ -1,74 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Utility functions for creating schedules and samplers from config.
-"""
-
-import torch
-from omegaconf import DictConfig
-
-from .samplers.base import Sampler
-from .samplers.euler import EulerSampler
-from .schedules.base import Schedule
-from .schedules.lerp import LinearInterpolationSchedule
-from .timesteps.base import SamplingTimesteps
-from .timesteps.sampling.trailing import UniformTrailingSamplingTimesteps
-
-
-def create_schedule_from_config(
-    config: DictConfig,
-    device: torch.device,
-    dtype: torch.dtype = torch.float32,
-) -> Schedule:
-    """
-    Create a schedule from configuration.
-    """
-    if config.type == "lerp":
-        return LinearInterpolationSchedule(T=config.get("T", 1.0))
-
-    raise NotImplementedError
-
-
-def create_sampler_from_config(
-    config: DictConfig,
-    schedule: Schedule,
-    timesteps: SamplingTimesteps,
-) -> Sampler:
-    """
-    Create a sampler from configuration.
-    """
-    if config.type == "euler":
-        return EulerSampler(
-            schedule=schedule,
-            timesteps=timesteps,
-            prediction_type=config.prediction_type,
-        )
-    raise NotImplementedError
-
-
-def create_sampling_timesteps_from_config(
-    config: DictConfig,
-    schedule: Schedule,
-    device: torch.device,
-    dtype: torch.dtype = torch.float32,
-) -> SamplingTimesteps:
-    if config.type == "uniform_trailing":
-        return UniformTrailingSamplingTimesteps(
-            T=schedule.T,
-            steps=config.steps,
-            shift=config.get("shift", 1.0),
-            device=device,
-        )
-    raise NotImplementedError

commonx/diffusion/samplers/base.py

@@ -1,108 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Sampler base class.
-"""
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from typing import Callable
-import torch
-from tqdm import tqdm
-
-from ..schedules.base import Schedule
-from ..timesteps.base import SamplingTimesteps
-from ..types import PredictionType, SamplingDirection
-from ..utils import assert_schedule_timesteps_compatible
-
-
-@dataclass
-class SamplerModelArgs:
-    x_t: torch.Tensor
-    t: torch.Tensor
-    i: int
-
-
-class Sampler(ABC):
-    """
-    Samplers are ODE/SDE solvers.
-    """
-
-    def __init__(
-        self,
-        schedule: Schedule,
-        timesteps: SamplingTimesteps,
-        prediction_type: PredictionType,
-        return_endpoint: bool = True,
-    ):
-        assert_schedule_timesteps_compatible(
-            schedule=schedule,
-            timesteps=timesteps,
-        )
-        self.schedule = schedule
-        self.timesteps = timesteps
-        self.prediction_type = prediction_type
-        self.return_endpoint = return_endpoint
-
-    @abstractmethod
-    def sample(
-        self,
-        x: torch.Tensor,
-        f: Callable[[SamplerModelArgs], torch.Tensor],
-    ) -> torch.Tensor:
-        """
-        Generate a new sample given the the intial sample x and score function f.
-        """
-
-    def get_next_timestep(
-        self,
-        t: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Get the next sample timestep.
-        Support multiple different timesteps t in a batch.
-        If no more steps, return out of bound value -1 or T+1.
-        """
-        T = self.timesteps.T
-        steps = len(self.timesteps)
-        curr_idx = self.timesteps.index(t)
-        next_idx = curr_idx + 1
-        bound = -1 if self.timesteps.direction == SamplingDirection.backward else T + 1
-
-        s = self.timesteps[next_idx.clamp_max(steps - 1)]
-        s = s.where(next_idx < steps, bound)
-        return s
-
-    def get_endpoint(
-        self,
-        pred: torch.Tensor,
-        x_t: torch.Tensor,
-        t: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Get to the endpoint of the probability flow.
-        """
-        x_0, x_T = self.schedule.convert_from_pred(pred, self.prediction_type, x_t, t)
-        return x_0 if self.timesteps.direction == SamplingDirection.backward else x_T
-
-    def get_progress_bar(self):
-        """
-        Get progress bar for sampling.
-        """
-        return tqdm(
-            iterable=range(len(self.timesteps) - (0 if self.return_endpoint else 1)),
-            dynamic_ncols=True,
-            desc=self.__class__.__name__,
-        )

commonx/diffusion/samplers/euler.py

@@ -1,89 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-
-"""
-Euler ODE solver.
-"""
-
-from typing import Callable
-import torch
-from einops import rearrange
-from torch.nn import functional as F
-
-from models.dit_v2 import na
-
-from ..types import PredictionType
-from ..utils import expand_dims
-from .base import Sampler, SamplerModelArgs
-
-
-class EulerSampler(Sampler):
-    """
-    The Euler method is the simplest ODE solver.
-    <https://en.wikipedia.org/wiki/Euler_method>
-    """
-
-    def sample(
-        self,
-        x: torch.Tensor,
-        f: Callable[[SamplerModelArgs], torch.Tensor],
-    ) -> torch.Tensor:
-        timesteps = self.timesteps.timesteps
-        progress = self.get_progress_bar()
-        i = 0
-        for t, s in zip(timesteps[:-1], timesteps[1:]):
-            pred = f(SamplerModelArgs(x, t, i))
-            x = self.step_to(pred, x, t, s)
-            i += 1
-            progress.update()
-
-        if self.return_endpoint:
-            t = timesteps[-1]
-            pred = f(SamplerModelArgs(x, t, i))
-            x = self.get_endpoint(pred, x, t)
-            progress.update()
-        return x
-
-    def step(
-        self,
-        pred: torch.Tensor,
-        x_t: torch.Tensor,
-        t: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Step to the next timestep.
-        """
-        return self.step_to(pred, x_t, t, self.get_next_timestep(t))
-
-    def step_to(
-        self,
-        pred: torch.Tensor,
-        x_t: torch.Tensor,
-        t: torch.Tensor,
-        s: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Steps from x_t at timestep t to x_s at timestep s. Returns x_s.
-        """
-        t = expand_dims(t, x_t.ndim)
-        s = expand_dims(s, x_t.ndim)
-        T = self.schedule.T
-        # Step from x_t to x_s.
-        pred_x_0, pred_x_T = self.schedule.convert_from_pred(pred, self.prediction_type, x_t, t)
-        pred_x_s = self.schedule.forward(pred_x_0, pred_x_T, s.clamp(0, T))
-        # Clamp x_s to x_0 and x_T if s is out of bound.
-        pred_x_s = pred_x_s.where(s >= 0, pred_x_0)
-        pred_x_s = pred_x_s.where(s <= T, pred_x_T)
-        return pred_x_s

commonx/diffusion/schedules/base.py

@@ -1,131 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Schedule base class.
-"""
-
-from abc import ABC, abstractmethod, abstractproperty
-from typing import Tuple, Union
-import torch
-
-from ..types import PredictionType
-from ..utils import expand_dims
-
-
-class Schedule(ABC):
-    """
-    Diffusion schedules are uniquely defined by T, A, B:
-
-        x_t = A(t) * x_0 + B(t) * x_T, where t in [0, T]
-
-    Schedules can be continuous or discrete.
-    """
-
-    @abstractproperty
-    def T(self) -> Union[int, float]:
-        """
-        Maximum timestep inclusive.
-        Schedule is continuous if float, discrete if int.
-        """
-
-    @abstractmethod
-    def A(self, t: torch.Tensor) -> torch.Tensor:
-        """
-        Interpolation coefficient A.
-        Returns tensor with the same shape as t.
-        """
-
-    @abstractmethod
-    def B(self, t: torch.Tensor) -> torch.Tensor:
-        """
-        Interpolation coefficient B.
-        Returns tensor with the same shape as t.
-        """
-
-    # ----------------------------------------------------
-
-    def snr(self, t: torch.Tensor) -> torch.Tensor:
-        """
-        Signal to noise ratio.
-        Returns tensor with the same shape as t.
-        """
-        return (self.A(t) ** 2) / (self.B(t) ** 2)
-
-    def isnr(self, snr: torch.Tensor) -> torch.Tensor:
-        """
-        Inverse signal to noise ratio.
-        Returns tensor with the same shape as snr.
-        Subclass may implement.
-        """
-        raise NotImplementedError
-
-    # ----------------------------------------------------
-
-    def is_continuous(self) -> bool:
-        """
-        Whether the schedule is continuous.
-        """
-        return isinstance(self.T, float)
-
-    def forward(self, x_0: torch.Tensor, x_T: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
-        """
-        Diffusion forward function.
-        """
-        t = expand_dims(t, x_0.ndim)
-        return self.A(t) * x_0 + self.B(t) * x_T
-
-    def convert_from_pred(
-        self, pred: torch.Tensor, pred_type: PredictionType, x_t: torch.Tensor, t: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Convert from prediction. Return predicted x_0 and x_T.
-        """
-        t = expand_dims(t, x_t.ndim)
-        A_t = self.A(t)
-        B_t = self.B(t)
-
-        if pred_type == PredictionType.x_T:
-            pred_x_T = pred
-            pred_x_0 = (x_t - B_t * pred_x_T) / A_t
-        elif pred_type == PredictionType.x_0:
-            pred_x_0 = pred
-            pred_x_T = (x_t - A_t * pred_x_0) / B_t
-        elif pred_type == PredictionType.v_cos:
-            pred_x_0 = A_t * x_t - B_t * pred
-            pred_x_T = A_t * pred + B_t * x_t
-        elif pred_type == PredictionType.v_lerp:
-            pred_x_0 = (x_t - B_t * pred) / (A_t + B_t)
-            pred_x_T = (x_t + A_t * pred) / (A_t + B_t)
-        else:
-            raise NotImplementedError
-
-        return pred_x_0, pred_x_T
-
-    def convert_to_pred(
-        self, x_0: torch.Tensor, x_T: torch.Tensor, t: torch.Tensor, pred_type: PredictionType
-    ) -> torch.FloatTensor:
-        """
-        Convert to prediction target given x_0 and x_T.
-        """
-        if pred_type == PredictionType.x_T:
-            return x_T
-        if pred_type == PredictionType.x_0:
-            return x_0
-        if pred_type == PredictionType.v_cos:
-            t = expand_dims(t, x_0.ndim)
-            return self.A(t) * x_T - self.B(t) * x_0
-        if pred_type == PredictionType.v_lerp:
-            return x_T - x_0
-        raise NotImplementedError

commonx/diffusion/schedules/lerp.py

@@ -1,55 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Linear interpolation schedule (lerp).
-"""
-
-from typing import Union
-import torch
-
-from .base import Schedule
-
-
-class LinearInterpolationSchedule(Schedule):
-    """
-    Linear interpolation schedule (lerp) is proposed by flow matching and rectified flow.
-    It leads to straighter probability flow theoretically. It is also used by Stable Diffusion 3.
-    <https://arxiv.org/abs/2209.03003>
-    <https://arxiv.org/abs/2210.02747>
-
-        x_t = (1 - t) * x_0 + t * x_T
-
-    Can be either continuous or discrete.
-    """
-
-    def __init__(self, T: Union[int, float] = 1.0):
-        self._T = T
-
-    @property
-    def T(self) -> Union[int, float]:
-        return self._T
-
-    def A(self, t: torch.Tensor) -> torch.Tensor:
-        return 1 - (t / self.T)
-
-    def B(self, t: torch.Tensor) -> torch.Tensor:
-        return t / self.T
-
-    # ----------------------------------------------------
-
-    def isnr(self, snr: torch.Tensor) -> torch.Tensor:
-        t = self.T / (1 + snr**0.5)
-        t = t if self.is_continuous() else t.round().int()
-        return t

commonx/diffusion/timesteps/base.py

@@ -1,72 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Sequence, Union
-import torch
-
-from ..types import SamplingDirection
-
-
-class Timesteps(ABC):
-    """
-    Timesteps base class.
-    """
-
-    def __init__(self, T: Union[int, float]):
-        assert T > 0
-        self._T = T
-
-    @property
-    def T(self) -> Union[int, float]:
-        """
-        Maximum timestep inclusive.
-        int if discrete, float if continuous.
-        """
-        return self._T
-
-    def is_continuous(self) -> bool:
-        """
-        Whether the schedule is continuous.
-        """
-        return isinstance(self.T, float)
-
-
-class SamplingTimesteps(Timesteps):
-    """
-    Sampling timesteps.
-    It defines the discretization of sampling steps.
-    """
-
-    def __init__(
-        self,
-        T: Union[int, float],
-        timesteps: torch.Tensor,
-        direction: SamplingDirection,
-    ):
-        assert timesteps.ndim == 1
-        super().__init__(T)
-        self.timesteps = timesteps
-        self.direction = direction
-
-    def __len__(self) -> int:
-        """
-        Number of sampling steps.
-        """
-        return len(self.timesteps)
-
-    def __getitem__(self, idx: Union[int, torch.IntTensor]) -> torch.Tensor:
-        """
-        The timestep at the sampling step.
-        Returns a scalar tensor if idx is int,
-        or tensor of the same size if idx is a tensor.
-        """
-        return self.timesteps[idx]
-
-    def index(self, t: torch.Tensor) -> torch.Tensor:
-        """
-        Find index by t.
-        Return index of the same shape as t.
-        Index is -1 if t not found in timesteps.
-        """
-        i, j = t.reshape(-1, 1).eq(self.timesteps).nonzero(as_tuple=True)
-        idx = torch.full_like(t, fill_value=-1, dtype=torch.int)
-        idx.view(-1)[i] = j.int()
-        return idx

commonx/diffusion/timesteps/sampling/trailing.py

@@ -1,49 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-import torch
-
-from ...types import SamplingDirection
-from ..base import SamplingTimesteps
-
-
-class UniformTrailingSamplingTimesteps(SamplingTimesteps):
-    """
-    Uniform trailing sampling timesteps.
-    Defined in (https://arxiv.org/abs/2305.08891)
-
-    Shift is proposed in SD3 for RF schedule.
-    Defined in (https://arxiv.org/pdf/2403.03206) eq.23
-    """
-
-    def __init__(
-        self,
-        T: int,
-        steps: int,
-        shift: float = 1.0,
-        device: torch.device = "cpu",
-    ):
-        # Create trailing timesteps.
-        timesteps = torch.arange(1.0, 0.0, -1.0 / steps, device=device)
-
-        # Shift timesteps.
-        timesteps = shift * timesteps / (1 + (shift - 1) * timesteps)
-
-        # Scale to T range.
-        if isinstance(T, float):
-            timesteps = timesteps * T
-        else:
-            timesteps = timesteps.mul(T + 1).sub(1).round().int()
-
-        super().__init__(T=T, timesteps=timesteps, direction=SamplingDirection.backward)

commonx/diffusion/types.py

@@ -1,59 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Type definitions.
-"""
-
-from enum import Enum
-
-
-class PredictionType(str, Enum):
-    """
-    x_0:
-        Predict data sample.
-    x_T:
-        Predict noise sample.
-        Proposed by DDPM (https://arxiv.org/abs/2006.11239)
-        Proved problematic by zsnr paper (https://arxiv.org/abs/2305.08891)
-    v_cos:
-        Predict velocity dx/dt based on the cosine schedule (A_t * x_T - B_t * x_0).
-        Proposed by progressive distillation (https://arxiv.org/abs/2202.00512)
-    v_lerp:
-        Predict velocity dx/dt based on the lerp schedule (x_T - x_0).
-        Proposed by rectified flow (https://arxiv.org/abs/2209.03003)
-    """
-
-    x_0 = "x_0"
-    x_T = "x_T"
-    v_cos = "v_cos"
-    v_lerp = "v_lerp"
-
-
-class SamplingDirection(str, Enum):
-    """
-    backward: Sample from x_T to x_0 for data generation.
-    forward:  Sample from x_0 to x_T for noise inversion.
-    """
-
-    backward = "backward"
-    forward = "forward"
-
-    @staticmethod
-    def reverse(direction):
-        if direction == SamplingDirection.backward:
-            return SamplingDirection.forward
-        if direction == SamplingDirection.forward:
-            return SamplingDirection.backward
-        raise NotImplementedError

commonx/diffusion/utils.py

@@ -1,84 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Utility functions.
-"""
-
-from typing import Callable
-import torch
-
-
-def expand_dims(tensor: torch.Tensor, ndim: int):
-    """
-    Expand tensor to target ndim. New dims are added to the right.
-    For example, if the tensor shape was (8,), target ndim is 4, return (8, 1, 1, 1).
-    """
-    shape = tensor.shape + (1,) * (ndim - tensor.ndim)
-    return tensor.reshape(shape)
-
-
-def assert_schedule_timesteps_compatible(schedule, timesteps):
-    """
-    Check if schedule and timesteps are compatible.
-    """
-    if schedule.T != timesteps.T:
-        raise ValueError("Schedule and timesteps must have the same T.")
-    if schedule.is_continuous() != timesteps.is_continuous():
-        raise ValueError("Schedule and timesteps must have the same continuity.")
-
-
-def classifier_free_guidance(
-    pos: torch.Tensor,
-    neg: torch.Tensor,
-    scale: float,
-    rescale: float = 0.0,
-):
-    """
-    Apply classifier-free guidance.
-    """
-    # Classifier-free guidance (https://arxiv.org/abs/2207.12598)
-    cfg = neg + scale * (pos - neg)
-
-    # Classifier-free guidance rescale (https://arxiv.org/pdf/2305.08891.pdf)
-    if rescale != 0.0:
-        pos_std = pos.std(dim=list(range(1, pos.ndim)), keepdim=True)
-        cfg_std = cfg.std(dim=list(range(1, cfg.ndim)), keepdim=True)
-        factor = pos_std / cfg_std
-        factor = rescale * factor + (1 - rescale)
-        cfg *= factor
-
-    return cfg
-
-
-def classifier_free_guidance_dispatcher(
-    pos: Callable,
-    neg: Callable,
-    scale: float,
-    rescale: float = 0.0,
-):
-    """
-    Optionally execute models depending on classifer-free guidance scale.
-    """
-    # If scale is 1, no need to execute neg model.
-    if scale == 1.0:
-        return pos()
-
-    # Otherwise, execute both pos nad neg models and apply cfg.
-    return classifier_free_guidance(
-        pos=pos(),
-        neg=neg(),
-        scale=scale,
-        rescale=rescale,
-    )

commonx/distributed/__init__.py

@@ -1,37 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Distributed package.
-"""
-
-from .basic import (
-    barrier_if_distributed,
-    convert_to_ddp,
-    get_device,
-    get_global_rank,
-    get_local_rank,
-    get_world_size,
-    init_torch,
-)
-
-__all__ = [
-    "barrier_if_distributed",
-    "convert_to_ddp",
-    "get_device",
-    "get_global_rank",
-    "get_local_rank",
-    "get_world_size",
-    "init_torch",
-]

commonx/distributed/advanced.py

@@ -1,208 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Advanced distributed functions for sequence parallel.
-"""
-
-from typing import Optional, List
-import torch
-import torch.distributed as dist
-from torch.distributed.device_mesh import DeviceMesh, init_device_mesh
-from torch.distributed.fsdp import ShardingStrategy
-
-from .basic import get_global_rank, get_world_size
-
-
-_DATA_PARALLEL_GROUP = None
-_SEQUENCE_PARALLEL_GROUP = None
-_SEQUENCE_PARALLEL_CPU_GROUP = None
-_MODEL_SHARD_CPU_INTER_GROUP = None
-_MODEL_SHARD_CPU_INTRA_GROUP = None
-_MODEL_SHARD_INTER_GROUP = None
-_MODEL_SHARD_INTRA_GROUP = None
-_SEQUENCE_PARALLEL_GLOBAL_RANKS = None
-
-
-def get_data_parallel_group() -> Optional[dist.ProcessGroup]:
-    """
-    Get data parallel process group.
-    """
-    return _DATA_PARALLEL_GROUP
-
-
-def get_sequence_parallel_group() -> Optional[dist.ProcessGroup]:
-    """
-    Get sequence parallel process group.
-    """
-    return _SEQUENCE_PARALLEL_GROUP
-
-
-def get_sequence_parallel_cpu_group() -> Optional[dist.ProcessGroup]:
-    """
-    Get sequence parallel CPU process group.
-    """
-    return _SEQUENCE_PARALLEL_CPU_GROUP
-
-
-def get_data_parallel_rank() -> int:
-    """
-    Get data parallel rank.
-    """
-    group = get_data_parallel_group()
-    return dist.get_rank(group) if group else get_global_rank()
-
-
-def get_data_parallel_world_size() -> int:
-    """
-    Get data parallel world size.
-    """
-    group = get_data_parallel_group()
-    return dist.get_world_size(group) if group else get_world_size()
-
-
-def get_sequence_parallel_rank() -> int:
-    """
-    Get sequence parallel rank.
-    """
-    group = get_sequence_parallel_group()
-    return dist.get_rank(group) if group else 0
-
-
-def get_sequence_parallel_world_size() -> int:
-    """
-    Get sequence parallel world size.
-    """
-    group = get_sequence_parallel_group()
-    return dist.get_world_size(group) if group else 1
-
-
-def get_model_shard_cpu_intra_group() -> Optional[dist.ProcessGroup]:
-    """
-    Get the CPU intra process group of model sharding.
-    """
-    return _MODEL_SHARD_CPU_INTRA_GROUP
-
-
-def get_model_shard_cpu_inter_group() -> Optional[dist.ProcessGroup]:
-    """
-    Get the CPU inter process group of model sharding.
-    """
-    return _MODEL_SHARD_CPU_INTER_GROUP
-
-
-def get_model_shard_intra_group() -> Optional[dist.ProcessGroup]:
-    """
-    Get the GPU intra process group of model sharding.
-    """
-    return _MODEL_SHARD_INTRA_GROUP
-
-
-def get_model_shard_inter_group() -> Optional[dist.ProcessGroup]:
-    """
-    Get the GPU inter process group of model sharding.
-    """
-    return _MODEL_SHARD_INTER_GROUP
-
-
-def init_sequence_parallel(sequence_parallel_size: int):
-    """
-    Initialize sequence parallel.
-    """
-    global _DATA_PARALLEL_GROUP
-    global _SEQUENCE_PARALLEL_GROUP
-    global _SEQUENCE_PARALLEL_CPU_GROUP
-    global _SEQUENCE_PARALLEL_GLOBAL_RANKS
-    assert dist.is_initialized()
-    world_size = dist.get_world_size()
-    rank = dist.get_rank()
-    data_parallel_size = world_size // sequence_parallel_size
-    for i in range(data_parallel_size):
-        start_rank = i * sequence_parallel_size
-        end_rank = (i + 1) * sequence_parallel_size
-        ranks = range(start_rank, end_rank)
-        group = dist.new_group(ranks)
-        cpu_group = dist.new_group(ranks, backend="gloo")
-        if rank in ranks:
-            _SEQUENCE_PARALLEL_GROUP = group
-            _SEQUENCE_PARALLEL_CPU_GROUP = cpu_group
-            _SEQUENCE_PARALLEL_GLOBAL_RANKS = list(ranks)
-
-
-def init_model_shard_group(
-    *,
-    sharding_strategy: ShardingStrategy,
-    device_mesh: Optional[DeviceMesh] = None,
-):
-    """
-    Initialize process group of model sharding.
-    """
-    global _MODEL_SHARD_INTER_GROUP
-    global _MODEL_SHARD_INTRA_GROUP
-    global _MODEL_SHARD_CPU_INTER_GROUP
-    global _MODEL_SHARD_CPU_INTRA_GROUP
-    assert dist.is_initialized()
-    world_size = dist.get_world_size()
-    if device_mesh is not None:
-        num_shards_per_group = device_mesh.shape[1]
-    elif sharding_strategy == ShardingStrategy.NO_SHARD:
-        num_shards_per_group = 1
-    elif sharding_strategy in [
-        ShardingStrategy.HYBRID_SHARD,
-        ShardingStrategy._HYBRID_SHARD_ZERO2,
-    ]:
-        num_shards_per_group = torch.cuda.device_count()
-    else:
-        num_shards_per_group = world_size
-    num_groups = world_size // num_shards_per_group
-    device_mesh = (num_groups, num_shards_per_group)
-
-    gpu_mesh_2d = init_device_mesh("cuda", device_mesh, mesh_dim_names=("inter", "intra"))
-    cpu_mesh_2d = init_device_mesh("cpu", device_mesh, mesh_dim_names=("inter", "intra"))
-
-    _MODEL_SHARD_INTER_GROUP = gpu_mesh_2d.get_group("inter")
-    _MODEL_SHARD_INTRA_GROUP = gpu_mesh_2d.get_group("intra")
-    _MODEL_SHARD_CPU_INTER_GROUP = cpu_mesh_2d.get_group("inter")
-    _MODEL_SHARD_CPU_INTRA_GROUP = cpu_mesh_2d.get_group("intra")
-
-def get_sequence_parallel_global_ranks() -> List[int]:
-    """
-    Get all global ranks of the sequence parallel process group
-    that the caller rank belongs to.
-    """
-    if _SEQUENCE_PARALLEL_GLOBAL_RANKS is None:
-        return [dist.get_rank()]
-    return _SEQUENCE_PARALLEL_GLOBAL_RANKS
-
-
-def get_next_sequence_parallel_rank() -> int:
-    """
-    Get the next global rank of the sequence parallel process group
-    that the caller rank belongs to.
-    """
-    sp_global_ranks = get_sequence_parallel_global_ranks()
-    sp_rank = get_sequence_parallel_rank()
-    sp_size = get_sequence_parallel_world_size()
-    return sp_global_ranks[(sp_rank + 1) % sp_size]
-
-
-def get_prev_sequence_parallel_rank() -> int:
-    """
-    Get the previous global rank of the sequence parallel process group
-    that the caller rank belongs to.
-    """
-    sp_global_ranks = get_sequence_parallel_global_ranks()
-    sp_rank = get_sequence_parallel_rank()
-    sp_size = get_sequence_parallel_world_size()
-    return sp_global_ranks[(sp_rank + sp_size - 1) % sp_size]

commonx/distributed/basic.py

@@ -1,84 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Distributed basic functions.
-"""
-
-import os
-from datetime import timedelta
-import torch
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel
-
-
-def get_global_rank() -> int:
-    """
-    Get the global rank, the global index of the GPU.
-    """
-    return int(os.environ.get("RANK", "0"))
-
-
-def get_local_rank() -> int:
-    """
-    Get the local rank, the local index of the GPU.
-    """
-    return int(os.environ.get("LOCAL_RANK", "0"))
-
-
-def get_world_size() -> int:
-    """
-    Get the world size, the total amount of GPUs.
-    """
-    return int(os.environ.get("WORLD_SIZE", "1"))
-
-
-def get_device() -> torch.device:
-    """
-    Get current rank device.
-    """
-    return torch.device("cuda", get_local_rank())
-
-
-def barrier_if_distributed(*args, **kwargs):
-    """
-    Synchronizes all processes if under distributed context.
-    """
-    if dist.is_initialized():
-        return dist.barrier(*args, **kwargs)
-
-
-def init_torch(cudnn_benchmark=True, timeout=timedelta(seconds=600)):
-    """
-    Common PyTorch initialization configuration.
-    """
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cudnn.allow_tf32 = True
-    torch.backends.cudnn.benchmark = cudnn_benchmark
-    torch.cuda.set_device(get_local_rank())
-    dist.init_process_group(
-        backend="nccl",
-        rank=get_global_rank(),
-        world_size=get_world_size(),
-        timeout=timeout,
-    )
-
-
-def convert_to_ddp(module: torch.nn.Module, **kwargs) -> DistributedDataParallel:
-    return DistributedDataParallel(
-        module=module,
-        device_ids=[get_local_rank()],
-        output_device=get_local_rank(),
-        **kwargs,
-    )

commonx/distributed/meta_init_utils.py

@@ -1,41 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-import torch
-from rotary_embedding_torch import RotaryEmbedding
-from torch import nn
-from torch.distributed.fsdp._common_utils import _is_fsdp_flattened
-
-__all__ = ["meta_non_persistent_buffer_init_fn"]
-
-
-def meta_non_persistent_buffer_init_fn(module: nn.Module) -> nn.Module:
-    """
-    Used for materializing `non-persistent tensor buffers` while model resuming.
-
-    Since non-persistent tensor buffers are not saved in state_dict,
-    when initializing model with meta device, user should materialize those buffers manually.
-
-    Currently, only `rope.dummy` is this special case.
-    """
-    with torch.no_grad():
-        for submodule in module.modules():
-            if not isinstance(submodule, RotaryEmbedding):
-                continue
-            for buffer_name, buffer in submodule.named_buffers(recurse=False):
-                if buffer.is_meta and "dummy" in buffer_name:
-                    materialized_buffer = torch.zeros_like(buffer, device="cpu")
-                    setattr(submodule, buffer_name, materialized_buffer)
-    assert not any(b.is_meta for n, b in module.named_buffers())
-    return module

commonx/distributed/ops.py

@@ -1,494 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Distributed ops for supporting sequence parallel.
-"""
-
-from collections import defaultdict
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-import torch
-import torch.distributed as dist
-from torch import Tensor
-
-from common.cache import Cache
-from common.distributed.advanced import (
-    get_sequence_parallel_group,
-    get_sequence_parallel_rank,
-    get_sequence_parallel_world_size,
-)
-
-from .basic import get_device
-
-_SEQ_DATA_BUF = defaultdict(lambda: [None, None, None])
-_SEQ_DATA_META_SHAPES = defaultdict()
-_SEQ_DATA_META_DTYPES = defaultdict()
-_SEQ_DATA_ASYNC_COMMS = defaultdict(list)
-_SYNC_BUFFER = defaultdict(dict)
-
-
-def single_all_to_all(
-    local_input: Tensor,
-    scatter_dim: int,
-    gather_dim: int,
-    group: dist.ProcessGroup,
-    async_op: bool = False,
-):
-    """
-    A function to do all-to-all on a tensor
-    """
-    seq_world_size = dist.get_world_size(group)
-    prev_scatter_dim = scatter_dim
-    if scatter_dim != 0:
-        local_input = local_input.transpose(0, scatter_dim)
-        if gather_dim == 0:
-            gather_dim = scatter_dim
-        scatter_dim = 0
-
-    inp_shape = list(local_input.shape)
-    inp_shape[scatter_dim] = inp_shape[scatter_dim] // seq_world_size
-    input_t = local_input.reshape(
-        [seq_world_size, inp_shape[scatter_dim]] + inp_shape[scatter_dim + 1 :]
-    ).contiguous()
-    output = torch.empty_like(input_t)
-    comm = dist.all_to_all_single(output, input_t, group=group, async_op=async_op)
-    if async_op:
-        # let user's code transpose & reshape
-        return output, comm, prev_scatter_dim
-
-    # first dim is seq_world_size, so we can split it directly
-    output = torch.cat(output.split(1), dim=gather_dim + 1).squeeze(0)
-    if prev_scatter_dim:
-        output = output.transpose(0, prev_scatter_dim).contiguous()
-    return output
-
-
-def _all_to_all(
-    local_input: Tensor,
-    scatter_dim: int,
-    gather_dim: int,
-    group: dist.ProcessGroup,
-):
-    seq_world_size = dist.get_world_size(group)
-    input_list = [
-        t.contiguous() for t in torch.tensor_split(local_input, seq_world_size, scatter_dim)
-    ]
-    output_list = [torch.empty_like(input_list[0]) for _ in range(seq_world_size)]
-    dist.all_to_all(output_list, input_list, group=group)
-    return torch.cat(output_list, dim=gather_dim).contiguous()
-
-
-class SeqAllToAll(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx: Any,
-        group: dist.ProcessGroup,
-        local_input: Tensor,
-        scatter_dim: int,
-        gather_dim: int,
-        async_op: bool,
-    ) -> Tensor:
-        ctx.group = group
-        ctx.scatter_dim = scatter_dim
-        ctx.gather_dim = gather_dim
-        ctx.async_op = async_op
-        if async_op:
-            output, comm, prev_scatter_dim = single_all_to_all(
-                local_input, scatter_dim, gather_dim, group, async_op=async_op
-            )
-            ctx.prev_scatter_dim = prev_scatter_dim
-            return output, comm
-
-        return _all_to_all(local_input, scatter_dim, gather_dim, group)
-
-    @staticmethod
-    def backward(ctx: Any, *grad_output: Tensor) -> Tuple[None, Tensor, None, None]:
-        if ctx.async_op:
-            input_t = torch.cat(grad_output[0].split(1), dim=ctx.gather_dim + 1).squeeze(0)
-            if ctx.prev_scatter_dim:
-                input_t = input_t.transpose(0, ctx.prev_scatter_dim)
-        else:
-            input_t = grad_output[0]
-        return (
-            None,
-            _all_to_all(input_t, ctx.gather_dim, ctx.scatter_dim, ctx.group),
-            None,
-            None,
-            None,
-        )
-
-
-class Slice(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx: Any, group: dist.ProcessGroup, local_input: Tensor, dim: int) -> Tensor:
-        ctx.group = group
-        ctx.rank = dist.get_rank(group)
-        seq_world_size = dist.get_world_size(group)
-        ctx.seq_world_size = seq_world_size
-        ctx.dim = dim
-        dim_size = local_input.shape[dim]
-        return local_input.split(dim_size // seq_world_size, dim=dim)[ctx.rank].contiguous()
-
-    @staticmethod
-    def backward(ctx: Any, grad_output: Tensor) -> Tuple[None, Tensor, None]:
-        dim_size = list(grad_output.size())
-        split_size = dim_size[0]
-        dim_size[0] = dim_size[0] * ctx.seq_world_size
-        output = torch.empty(dim_size, dtype=grad_output.dtype, device=torch.cuda.current_device())
-        dist._all_gather_base(output, grad_output, group=ctx.group)
-        return (None, torch.cat(output.split(split_size), dim=ctx.dim), None)
-
-
-class Gather(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx: Any,
-        group: dist.ProcessGroup,
-        local_input: Tensor,
-        dim: int,
-        grad_scale: Optional[bool] = False,
-    ) -> Tensor:
-        ctx.group = group
-        ctx.rank = dist.get_rank(group)
-        ctx.dim = dim
-        ctx.grad_scale = grad_scale
-        seq_world_size = dist.get_world_size(group)
-        ctx.seq_world_size = seq_world_size
-        dim_size = list(local_input.size())
-        split_size = dim_size[0]
-        ctx.part_size = dim_size[dim]
-        dim_size[0] = dim_size[0] * seq_world_size
-        output = torch.empty(dim_size, dtype=local_input.dtype, device=torch.cuda.current_device())
-        dist._all_gather_base(output, local_input.contiguous(), group=ctx.group)
-        return torch.cat(output.split(split_size), dim=dim)
-
-    @staticmethod
-    def backward(ctx: Any, grad_output: Tensor) -> Tuple[None, Tensor]:
-        if ctx.grad_scale:
-            grad_output = grad_output * ctx.seq_world_size
-        return (
-            None,
-            grad_output.split(ctx.part_size, dim=ctx.dim)[ctx.rank].contiguous(),
-            None,
-            None,
-        )
-
-
-def gather_seq_scatter_heads_qkv(
-    qkv_tensor: Tensor,
-    *,
-    seq_dim: int,
-    qkv_shape: Optional[Tensor] = None,
-    cache: Cache = Cache(disable=True),
-    restore_shape: bool = True,
-):
-    """
-    A func to sync splited qkv tensor
-    qkv_tensor: the tensor we want to do alltoall with. The last dim must
-        be the projection_idx, which we will split into 3 part. After
-        spliting, the gather idx will be projecttion_idx + 1
-    seq_dim: gather_dim for all2all comm
-    restore_shape: if True, output will has the same shape length as input
-    """
-    group = get_sequence_parallel_group()
-    if not group:
-        return qkv_tensor
-    world = get_sequence_parallel_world_size()
-    orig_shape = qkv_tensor.shape
-    scatter_dim = qkv_tensor.dim()
-    bef_all2all_shape = list(orig_shape)
-    qkv_proj_dim = bef_all2all_shape[-1]
-    bef_all2all_shape = bef_all2all_shape[:-1] + [3, qkv_proj_dim // 3]
-    qkv_tensor = qkv_tensor.view(bef_all2all_shape)
-    qkv_tensor = SeqAllToAll.apply(group, qkv_tensor, scatter_dim, seq_dim, False)
-    if restore_shape:
-        out_shape = list(orig_shape)
-        out_shape[seq_dim] *= world
-        out_shape[-1] = qkv_proj_dim // world
-        qkv_tensor = qkv_tensor.view(out_shape)
-
-    # remove padding
-    if qkv_shape is not None:
-        unpad_dim_size = cache(
-            "unpad_dim_size", lambda: torch.sum(torch.prod(qkv_shape, dim=-1)).item()
-        )
-        if unpad_dim_size % world != 0:
-            padding_size = qkv_tensor.size(seq_dim) - unpad_dim_size
-            qkv_tensor = _unpad_tensor(qkv_tensor, seq_dim, padding_size)
-    return qkv_tensor
-
-
-def slice_inputs(x: Tensor, dim: int, padding: bool = True):
-    """
-    A func to slice the input sequence in sequence parallel
-    """
-    group = get_sequence_parallel_group()
-    if group is None:
-        return x
-    sp_rank = get_sequence_parallel_rank()
-    sp_world = get_sequence_parallel_world_size()
-    dim_size = x.shape[dim]
-    unit = (dim_size + sp_world - 1) // sp_world
-    if padding and dim_size % sp_world:
-        padding_size = sp_world - (dim_size % sp_world)
-        x = _pad_tensor(x, dim, padding_size)
-    slc = [slice(None)] * len(x.shape)
-    slc[dim] = slice(unit * sp_rank, unit * (sp_rank + 1))
-    return x[slc]
-
-
-def remove_seqeunce_parallel_padding(x: Tensor, dim: int, unpad_dim_size: int):
-    """
-    A func to remove the padding part of the tensor based on its original shape
-    """
-    group = get_sequence_parallel_group()
-    if group is None:
-        return x
-    sp_world = get_sequence_parallel_world_size()
-    if unpad_dim_size % sp_world == 0:
-        return x
-    padding_size = sp_world - (unpad_dim_size % sp_world)
-    assert (padding_size + unpad_dim_size) % sp_world == 0
-    return _unpad_tensor(x, dim=dim, padding_size=padding_size)
-
-
-def gather_heads_scatter_seq(x: Tensor, head_dim: int, seq_dim: int) -> Tensor:
-    """
-    A func to sync attention result with alltoall in sequence parallel
-    """
-    group = get_sequence_parallel_group()
-    if not group:
-        return x
-    dim_size = x.size(seq_dim)
-    sp_world = get_sequence_parallel_world_size()
-    if dim_size % sp_world != 0:
-        padding_size = sp_world - (dim_size % sp_world)
-        x = _pad_tensor(x, seq_dim, padding_size)
-    return SeqAllToAll.apply(group, x, seq_dim, head_dim, False)
-
-
-def gather_seq_scatter_heads(x: Tensor, seq_dim: int, head_dim: int) -> Tensor:
-    """
-    A func to sync embedding input with alltoall in sequence parallel
-    """
-    group = get_sequence_parallel_group()
-    if not group:
-        return x
-    return SeqAllToAll.apply(group, x, head_dim, seq_dim, False)
-
-
-def scatter_heads(x: Tensor, dim: int) -> Tensor:
-    """
-    A func to split heads before attention in sequence parallel
-    """
-    group = get_sequence_parallel_group()
-    if not group:
-        return x
-    return Slice.apply(group, x, dim)
-
-
-def gather_heads(x: Tensor, dim: int, grad_scale: Optional[bool] = False) -> Tensor:
-    """
-    A func to gather heads for the attention result in sequence parallel
-    """
-    group = get_sequence_parallel_group()
-    if not group:
-        return x
-    return Gather.apply(group, x, dim, grad_scale)
-
-
-def gather_outputs(
-    x: Tensor,
-    *,
-    gather_dim: int,
-    padding_dim: Optional[int] = None,
-    unpad_shape: Optional[Tensor] = None,
-    cache: Cache = Cache(disable=True),
-    scale_grad=True,
-):
-    """
-    A func to gather the outputs for the model result in sequence parallel
-    """
-    group = get_sequence_parallel_group()
-    if not group:
-        return x
-    x = Gather.apply(group, x, gather_dim, scale_grad)
-    if padding_dim is not None:
-        unpad_dim_size = cache(
-            "unpad_dim_size", lambda: torch.sum(torch.prod(unpad_shape, dim=1)).item()
-        )
-        x = remove_seqeunce_parallel_padding(x, padding_dim, unpad_dim_size)
-    return x
-
-
-def _pad_tensor(x: Tensor, dim: int, padding_size: int):
-    shape = list(x.shape)
-    shape[dim] = padding_size
-    pad = torch.zeros(shape, dtype=x.dtype, device=x.device)
-    return torch.cat([x, pad], dim=dim)
-
-
-def _unpad_tensor(x: Tensor, dim: int, padding_size):
-    slc = [slice(None)] * len(x.shape)
-    slc[dim] = slice(0, -padding_size)
-    return x[slc]
-
-
-def _broadcast_data(data, shape, dtype, src, group, async_op):
-    comms = []
-    if isinstance(data, (list, tuple)):
-        for i, sub_shape in enumerate(shape):
-            comms += _broadcast_data(data[i], sub_shape, dtype[i], src, group, async_op)
-    elif isinstance(data, dict):
-        for key, sub_data in data.items():
-            comms += _broadcast_data(sub_data, shape[key], dtype[key], src, group, async_op)
-    elif isinstance(data, Tensor):
-        comms.append(dist.broadcast(data, src=src, group=group, async_op=async_op))
-    return comms
-
-
-def _traverse(data: Any, op: Callable) -> Union[None, List, Dict, Any]:
-    if isinstance(data, (list, tuple)):
-        return [_traverse(sub_data, op) for sub_data in data]
-    elif isinstance(data, dict):
-        return {key: _traverse(sub_data, op) for key, sub_data in data.items()}
-    elif isinstance(data, Tensor):
-        return op(data)
-    else:
-        return None
-
-
-def _get_shapes(data):
-    return _traverse(data, op=lambda x: x.shape)
-
-
-def _get_dtypes(data):
-    return _traverse(data, op=lambda x: x.dtype)
-
-
-def _construct_broadcast_buffer(shapes, dtypes, device):
-    if isinstance(shapes, torch.Size):
-        return torch.empty(shapes, dtype=dtypes, device=device)
-
-    if isinstance(shapes, (list, tuple)):
-        buffer = []
-        for i, sub_shape in enumerate(shapes):
-            buffer.append(_construct_broadcast_buffer(sub_shape, dtypes[i], device))
-    elif isinstance(shapes, dict):
-        buffer = {}
-        for key, sub_shape in shapes.items():
-            buffer[key] = _construct_broadcast_buffer(sub_shape, dtypes[key], device)
-    else:
-        return None
-    return buffer
-
-
-class SPDistForward:
-    """A forward tool to sync different result across sp group
-
-    Args:
-        module: a function or module to process users input
-        sp_step: current training step to judge which rank to broadcast its result to all
-        name: a distinct str to save meta and async comm
-        comm_shape: if different ranks have different shape, mark this arg to True
-        device: the device for current rank, can be empty
-    """
-
-    def __init__(
-        self,
-        name: str,
-        comm_shape: bool,
-        device: torch.device = None,
-    ):
-        self.name = name
-        self.comm_shape = comm_shape
-        if device:
-            self.device = device
-        else:
-            self.device = get_device()
-
-    def __call__(self, inputs) -> Any:
-        group = get_sequence_parallel_group()
-        if not group:
-            yield inputs
-        else:
-            device = self.device
-            sp_world = get_sequence_parallel_world_size()
-            sp_rank = get_sequence_parallel_rank()
-            for local_step in range(sp_world):
-                src_rank = dist.get_global_rank(group, local_step)
-                is_src = sp_rank == local_step
-                local_shapes = []
-                local_dtypes = []
-                if local_step == 0:
-                    local_result = inputs
-                    _SEQ_DATA_BUF[self.name][-1] = local_result
-                    local_shapes = _get_shapes(local_result)
-                    local_dtypes = _get_dtypes(local_result)
-                    if self.comm_shape:
-                        group_shapes_lists = [None] * sp_world
-                        dist.all_gather_object(group_shapes_lists, local_shapes, group=group)
-                        _SEQ_DATA_META_SHAPES[self.name] = group_shapes_lists
-                    else:
-                        _SEQ_DATA_META_SHAPES[self.name] = [local_shapes] * sp_world
-                    _SEQ_DATA_META_DTYPES[self.name] = local_dtypes
-                shapes = _SEQ_DATA_META_SHAPES[self.name][local_step]
-                dtypes = _SEQ_DATA_META_DTYPES[self.name]
-                buf_id = local_step % 2
-                if local_step == 0:
-                    sync_data = (
-                        local_result
-                        if is_src
-                        else _construct_broadcast_buffer(shapes, dtypes, device)
-                    )
-                    _broadcast_data(sync_data, shapes, dtypes, src_rank, group, False)
-                    _SEQ_DATA_BUF[self.name][buf_id] = sync_data
-
-                # wait for async comm ops
-                if _SEQ_DATA_ASYNC_COMMS[self.name]:
-                    for comm in _SEQ_DATA_ASYNC_COMMS[self.name]:
-                        comm.wait()
-                # before return the sync result, do async broadcast for next batch
-                if local_step < sp_world - 1:
-                    next_buf_id = 1 - buf_id
-                    shapes = _SEQ_DATA_META_SHAPES[self.name][local_step + 1]
-                    src_rank = dist.get_global_rank(group, local_step + 1)
-                    is_src = sp_rank == local_step + 1
-                    next_sync_data = (
-                        _SEQ_DATA_BUF[self.name][-1]
-                        if is_src
-                        else _construct_broadcast_buffer(shapes, dtypes, device)
-                    )
-                    _SEQ_DATA_ASYNC_COMMS[self.name] = _broadcast_data(
-                        next_sync_data, shapes, dtypes, src_rank, group, True
-                    )
-                    _SEQ_DATA_BUF[self.name][next_buf_id] = next_sync_data
-                yield _SEQ_DATA_BUF[self.name][buf_id]
-
-
-sync_inputs = SPDistForward(name="bef_fwd", comm_shape=True)
-
-
-def sync_data(data, sp_idx, name="tmp"):
-    group = get_sequence_parallel_group()
-    if group is None:
-        return data
-    # if sp_idx in _SYNC_BUFFER[name]:
-    #     return _SYNC_BUFFER[name][sp_idx]
-    sp_rank = get_sequence_parallel_rank()
-    src_rank = dist.get_global_rank(group, sp_idx)
-    objects = [data] if sp_rank == sp_idx else [None]
-    dist.broadcast_object_list(objects, src=src_rank, group=group)
-    # _SYNC_BUFFER[name] = {sp_idx: objects[0]}
-    return objects[0]

commonx/logger.py

@@ -1,44 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Logging utility functions.
-"""
-
-import logging
-import sys
-from typing import Optional
-
-from common.distributed import get_global_rank, get_local_rank, get_world_size
-
-_default_handler = logging.StreamHandler(sys.stdout)
-_default_handler.setFormatter(
-    logging.Formatter(
-        "%(asctime)s "
-        + (f"[Rank:{get_global_rank()}]" if get_world_size() > 1 else "")
-        + (f"[LocalRank:{get_local_rank()}]" if get_world_size() > 1 else "")
-        + "[%(threadName).12s][%(name)s][%(levelname).5s] "
-        + "%(message)s"
-    )
-)
-
-
-def get_logger(name: Optional[str] = None) -> logging.Logger:
-    """
-    Get a logger.
-    """
-    logger = logging.getLogger(name)
-    logger.addHandler(_default_handler)
-    logger.setLevel(logging.INFO)
-    return logger

commonx/partition.py

@@ -1,59 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-"""
-Partition utility functions.
-"""
-
-from typing import Any, List
-
-
-def partition_by_size(data: List[Any], size: int) -> List[List[Any]]:
-    """
-    Partition a list by size.
-    When indivisible, the last group contains fewer items than the target size.
-
-    Examples:
-        - data: [1,2,3,4,5]
-        - size: 2
-        - return: [[1,2], [3,4], [5]]
-    """
-    assert size > 0
-    return [data[i : (i + size)] for i in range(0, len(data), size)]
-
-
-def partition_by_groups(data: List[Any], groups: int) -> List[List[Any]]:
-    """
-    Partition a list by groups.
-    When indivisible, some groups may have more items than others.
-
-    Examples:
-        - data: [1,2,3,4,5]
-        - groups: 2
-        - return: [[1,3,5], [2,4]]
-    """
-    assert groups > 0
-    return [data[i::groups] for i in range(groups)]
-
-
-def shift_list(data: List[Any], n: int) -> List[Any]:
-    """
-    Rotate a list by n elements.
-
-    Examples:
-        - data: [1,2,3,4,5]
-        - n: 3
-        - return: [4,5,1,2,3]
-    """
-    return data[(n % len(data)) :] + data[: (n % len(data))]

commonx/seed.py

@@ -1,30 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-import random
-from typing import Optional
-import numpy as np
-import torch
-
-from common.distributed import get_global_rank
-
-
-def set_seed(seed: Optional[int], same_across_ranks: bool = False):
-    """Function that sets the seed for pseudo-random number generators."""
-    if seed is not None:
-        seed += get_global_rank() if not same_across_ranks else 0
-        random.seed(seed)
-        np.random.seed(seed)
-        torch.manual_seed(seed)
-

configs_3bx/main.yaml

@@ -1,88 +0,0 @@
-__object__:
-  path: projects.video_diffusion_sr.train
-  name: VideoDiffusionTrainer
-
-dit:
-  model:
-    __object__:
-      path: models.dit_v2.nadit
-      name: NaDiT
-      args: as_params
-    vid_in_channels: 33
-    vid_out_channels: 16
-    vid_dim: 2560
-    vid_out_norm: fusedrms
-    txt_in_dim: 5120
-    txt_in_norm: fusedln
-    txt_dim: ${.vid_dim}
-    emb_dim: ${eval:'6 * ${.vid_dim}'}
-    heads: 20
-    head_dim: 128 # llm-like
-    expand_ratio: 4
-    norm: fusedrms
-    norm_eps: 1.0e-05
-    ada: single
-    qk_bias: False
-    qk_norm: fusedrms
-    patch_size: [ 1,2,2 ]
-    num_layers: 32 # llm-like
-    mm_layers: 10
-    mlp_type: swiglu
-    msa_type: None
-    block_type: ${eval:'${.num_layers} * ["mmdit_sr"]'} # space-full
-    window: ${eval:'${.num_layers} * [(4,3,3)]'} # space-full
-    window_method: ${eval:'${.num_layers} // 2 * ["720pwin_by_size_bysize","720pswin_by_size_bysize"]'} # space-full
-    rope_type: mmrope3d
-    rope_dim: 128
-  compile: False
-  gradient_checkpoint: True
-  fsdp:
-    sharding_strategy: _HYBRID_SHARD_ZERO2
-
-ema:
-  decay: 0.9998
-
-vae:
-  model:
-    __inherit__: models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml
-    freeze_encoder: False
-    # gradient_checkpoint: True
-  slicing:
-    split_size: 4
-    memory_device: same
-  memory_limit:
-    conv_max_mem: 0.5
-    norm_max_mem: 0.5
-  checkpoint: ./ckpts/ema_vae.pth
-  scaling_factor: 0.9152
-  compile: False
-  grouping: False
-  dtype: bfloat16
-
-diffusion:
-  schedule:
-    type: lerp
-    T: 1000.0
-  sampler:
-    type: euler
-    prediction_type: v_lerp
-  timesteps:
-    training:
-      type: logitnormal
-      loc: 0.0
-      scale: 1.0
-    sampling:
-      type: uniform_trailing
-      steps: 50
-    transform: True
-  loss:
-    type: v_lerp
-  cfg:
-    scale: 7.5
-    rescale: 0
-
-condition:
-  i2v: 0.0
-  v2v: 0.0
-  sr: 1.0
-  noise_scale: 0.25

configs_7bx/main.yaml

@@ -1,85 +0,0 @@
-__object__:
-  path: projects.video_diffusion_sr.train
-  name: VideoDiffusionTrainer
-
-dit:
-  model:
-    __object__:
-      path: models.dit.nadit
-      name: NaDiT
-      args: as_params
-    vid_in_channels: 33
-    vid_out_channels: 16
-    vid_dim: 3072
-    txt_in_dim: 5120
-    txt_dim: ${.vid_dim}
-    emb_dim: ${eval:'6 * ${.vid_dim}'}
-    heads: 24
-    head_dim: 128 # llm-like
-    expand_ratio: 4
-    norm: fusedrms
-    norm_eps: 1e-5
-    ada: single
-    qk_bias: False
-    qk_rope: True
-    qk_norm: fusedrms
-    patch_size: [ 1,2,2 ]
-    num_layers: 36 # llm-like
-    shared_mlp: False
-    shared_qkv: False
-    mlp_type: normal
-    block_type: ${eval:'${.num_layers} * ["mmdit_sr"]'} # space-full
-    window: ${eval:'${.num_layers} * [(4,3,3)]'} # space-full
-    window_method: ${eval:'${.num_layers} // 2 * ["720pwin_by_size_bysize","720pswin_by_size_bysize"]'} # space-full
-  compile: False
-  gradient_checkpoint: True
-  fsdp:
-    sharding_strategy: _HYBRID_SHARD_ZERO2
-
-ema:
-  decay: 0.9998
-
-vae:
-  model:
-    __inherit__: models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml
-    freeze_encoder: False
-    # gradient_checkpoint: True
-  slicing:
-    split_size: 4
-    memory_device: same
-  memory_limit:
-    conv_max_mem: 0.5
-    norm_max_mem: 0.5
-  checkpoint: ./ckpts/ema_vae.pth
-  scaling_factor: 0.9152
-  compile: False
-  grouping: False
-  dtype: bfloat16
-
-diffusion:
-  schedule:
-    type: lerp
-    T: 1000.0
-  sampler:
-    type: euler
-    prediction_type: v_lerp
-  timesteps:
-    training:
-      type: logitnormal
-      loc: 0.0
-      scale: 1.0
-    sampling:
-      type: uniform_trailing
-      steps: 50
-    transform: True
-  loss:
-    type: v_lerp
-  cfg:
-    scale: 7.5
-    rescale: 0
-
-condition:
-  i2v: 0.0
-  v2v: 0.0
-  sr: 1.0
-  noise_scale: 0.25

datax/image/transforms/area_resize.py

@@ -1,135 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-import math
-import random
-from typing import Union
-import torch
-from PIL import Image
-from torchvision.transforms import functional as TVF
-from torchvision.transforms.functional import InterpolationMode
-
-
-class AreaResize:
-    def __init__(
-        self,
-        max_area: float,
-        downsample_only: bool = False,
-        interpolation: InterpolationMode = InterpolationMode.BICUBIC,
-    ):
-        self.max_area = max_area
-        self.downsample_only = downsample_only
-        self.interpolation = interpolation
-
-    def __call__(self, image: Union[torch.Tensor, Image.Image]):
-
-        if isinstance(image, torch.Tensor):
-            height, width = image.shape[-2:]
-        elif isinstance(image, Image.Image):
-            width, height = image.size
-        else:
-            raise NotImplementedError
-
-        scale = math.sqrt(self.max_area / (height * width))
-
-        # keep original height and width for small pictures.
-        scale = 1 if scale >= 1 and self.downsample_only else scale
-
-        resized_height, resized_width = round(height * scale), round(width * scale)
-
-        return TVF.resize(
-            image,
-            size=(resized_height, resized_width),
-            interpolation=self.interpolation,
-        )
-
-
-class AreaRandomCrop:
-    def __init__(
-        self,
-        max_area: float,
-    ):
-        self.max_area = max_area
-
-    def get_params(self, input_size, output_size):
-        """Get parameters for ``crop`` for a random crop.
-
-        Args:
-            img (PIL Image): Image to be cropped.
-            output_size (tuple): Expected output size of the crop.
-
-        Returns:
-            tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
-        """
-        # w, h = _get_image_size(img)
-        h, w = input_size
-        th, tw = output_size
-        if w <= tw and h <= th:
-            return 0, 0, h, w
-
-        i = random.randint(0, h - th)
-        j = random.randint(0, w - tw)
-        return i, j, th, tw
-
-    def __call__(self, image: Union[torch.Tensor, Image.Image]):
-        if isinstance(image, torch.Tensor):
-            height, width = image.shape[-2:]
-        elif isinstance(image, Image.Image):
-            width, height = image.size
-        else:
-            raise NotImplementedError
-
-        resized_height = math.sqrt(self.max_area / (width / height))
-        resized_width = (width / height) * resized_height
-
-        # print('>>>>>>>>>>>>>>>>>>>>>')
-        # print((height, width))
-        # print( (resized_height, resized_width))
-
-        resized_height, resized_width = round(resized_height), round(resized_width)
-        i, j, h, w = self.get_params((height, width), (resized_height, resized_width))
-        image = TVF.crop(image, i, j, h, w)
-        return image
-
-class ScaleResize:
-    def __init__(
-        self,
-        scale: float,
-    ):
-        self.scale = scale
-
-    def __call__(self, image: Union[torch.Tensor, Image.Image]):
-        if isinstance(image, torch.Tensor):
-            height, width = image.shape[-2:]
-            interpolation_mode = InterpolationMode.BILINEAR
-            antialias = True if image.ndim == 4 else "warn"
-        elif isinstance(image, Image.Image):
-            width, height = image.size
-            interpolation_mode = InterpolationMode.LANCZOS
-            antialias = "warn"
-        else:
-            raise NotImplementedError
-
-        scale = self.scale
-
-        # keep original height and width for small pictures
-
-        resized_height, resized_width = round(height * scale), round(width * scale)
-        image = TVF.resize(
-            image,
-            size=(resized_height, resized_width),
-            interpolation=interpolation_mode,
-            antialias=antialias,
-        )
-        return image

datax/image/transforms/divisible_crop.py

@@ -1,40 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Union
-import torch
-from PIL import Image
-from torchvision.transforms import functional as TVF
-
-
-class DivisibleCrop:
-    def __init__(self, factor):
-        if not isinstance(factor, tuple):
-            factor = (factor, factor)
-
-        self.height_factor, self.width_factor = factor[0], factor[1]
-
-    def __call__(self, image: Union[torch.Tensor, Image.Image]):
-        if isinstance(image, torch.Tensor):
-            height, width = image.shape[-2:]
-        elif isinstance(image, Image.Image):
-            width, height = image.size
-        else:
-            raise NotImplementedError
-
-        cropped_height = height - (height % self.height_factor)
-        cropped_width = width - (width % self.width_factor)
-
-        image = TVF.center_crop(img=image, output_size=(cropped_height, cropped_width))
-        return image

datax/image/transforms/na_resize.py

@@ -1,50 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Literal
-from torchvision.transforms import CenterCrop, Compose, InterpolationMode, Resize
-
-from .area_resize import AreaResize
-from .side_resize import SideResize
-
-
-def NaResize(
-    resolution: int,
-    mode: Literal["area", "side"],
-    downsample_only: bool,
-    interpolation: InterpolationMode = InterpolationMode.BICUBIC,
-):
-    if mode == "area":
-        return AreaResize(
-            max_area=resolution**2,
-            downsample_only=downsample_only,
-            interpolation=interpolation,
-        )
-    if mode == "side":
-        return SideResize(
-            size=resolution,
-            downsample_only=downsample_only,
-            interpolation=interpolation,
-        )
-    if mode == "square":
-        return Compose(
-            [
-                Resize(
-                    size=resolution,
-                    interpolation=interpolation,
-                ),
-                CenterCrop(resolution),
-            ]
-        )
-    raise ValueError(f"Unknown resize mode: {mode}")

datax/image/transforms/side_resize.py

@@ -1,54 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Union
-import torch
-from PIL import Image
-from torchvision.transforms import InterpolationMode
-from torchvision.transforms import functional as TVF
-
-
-class SideResize:
-    def __init__(
-        self,
-        size: int,
-        downsample_only: bool = False,
-        interpolation: InterpolationMode = InterpolationMode.BICUBIC,
-    ):
-        self.size = size
-        self.downsample_only = downsample_only
-        self.interpolation = interpolation
-
-    def __call__(self, image: Union[torch.Tensor, Image.Image]):
-        """
-        Args:
-            image (PIL Image or Tensor): Image to be scaled.
-
-        Returns:
-            PIL Image or Tensor: Rescaled image.
-        """
-        if isinstance(image, torch.Tensor):
-            height, width = image.shape[-2:]
-        elif isinstance(image, Image.Image):
-            width, height = image.size
-        else:
-            raise NotImplementedError
-
-        if self.downsample_only and min(width, height) < self.size:
-            # keep original height and width for small pictures.
-            size = min(width, height)
-        else:
-            size = self.size
-
-        return TVF.resize(image, size, self.interpolation)

datax/video/transforms/rearrange.py

@@ -1,24 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from einops import rearrange
-
-
-class Rearrange:
-    def __init__(self, pattern: str, **kwargs):
-        self.pattern = pattern
-        self.kwargs = kwargs
-
-    def __call__(self, x):
-        return rearrange(x, self.pattern, **self.kwargs)

environmentx.yml

@@ -1,238 +0,0 @@
-name: seedvr
-channels:
-  - defaults
-dependencies:
-  - _libgcc_mutex=0.1=main
-  - _openmp_mutex=5.1=1_gnu
-  - ld_impl_linux-64=2.38=h1181459_1
-  - libffi=3.4.4=h6a678d5_1
-  - libgcc-ng=11.2.0=h1234567_1
-  - libgomp=11.2.0=h1234567_1
-  - libstdcxx-ng=11.2.0=h1234567_1
-  - ncurses=6.4=h6a678d5_0
-  - openssl=3.0.14=h5eee18b_0
-  - pip=24.0=py39h06a4308_0
-  - python=3.9.19=h955ad1f_1
-  - readline=8.2=h5eee18b_0
-  - setuptools=69.5.1=py39h06a4308_0
-  - sqlite=3.45.3=h5eee18b_0
-  - tk=8.6.14=h39e8969_0
-  - tzdata=2024a=h04d1e81_0
-  - wheel=0.43.0=py39h06a4308_0
-  - xz=5.4.6=h5eee18b_1
-  - zlib=1.2.13=h5eee18b_1
-  - pip:
-      - absl-py==2.1.0
-      - accelerate==0.33.0
-      - addict==2.4.0
-      - antlr4-python3-runtime==4.9.3
-      - anykeystore==0.2
-      - apex==0.1
-      - asttokens==2.4.1
-      - astunparse==1.6.3
-      - attrs==23.2.0
-      - av==12.0.0
-      - basicsr==1.4.2
-      - beartype==0.18.5
-      - beautifulsoup4==4.12.3
-      - bitsandbytes==0.44.1
-      - black==24.4.2
-      - bs4==0.0.2
-      - bson==0.5.10
-      - certifi==2024.6.2
-      - cffi==1.16.0
-      - cfgv==3.4.0
-      - charset-normalizer==3.3.2
-      - click==8.1.7
-      - colorama==0.4.6
-      - contourpy==1.2.1
-      - cryptacular==1.6.2
-      - cryptography==39.0.2
-      - cycler==0.12.1
-      - decorator==4.4.2
-      - deepdiff==7.0.1
-      - deprecated==1.2.14
-      - diffusers==0.33.1
-      - distlib==0.3.8
-      - dnspython==2.6.1
-      - docker-pycreds==0.4.0
-      - docstring-parser==0.16
-      - einops==0.7.0
-      - exceptiongroup==1.2.1
-      - executing==2.0.1
-      - expecttest==0.2.1
-      - facexlib==0.3.0
-      - ffmpeg-python==0.2.0
-      - filelock==3.15.4
-      - filterpy==1.4.5
-      - flake8==7.1.0
-      - flash-attn==2.5.9.post1
-      - flatbuffers==24.3.25
-      - fonttools==4.53.0
-      - fsspec==2023.6.0
-      - ftfy==6.2.0
-      - future==1.0.0
-      - gast==0.5.4
-      - gdown==5.2.0
-      - gitdb==4.0.11
-      - gitpython==3.1.43
-      - google-pasta==0.2.0
-      - greenlet==3.0.3
-      - grpcio==1.64.1
-      - h5py==3.11.0
-      - hf-xet==1.1.2
-      - huggingface-hub==0.32.2
-      - hupper==1.12.1
-      - hypothesis==6.100.1
-      - icecream==2.1.3
-      - identify==2.5.36
-      - idna==3.7
-      - imageio==2.34.0
-      - imageio-ffmpeg==0.5.1
-      - importlib-metadata==7.2.1
-      - importlib-resources==6.4.0
-      - iniconfig==2.0.0
-      - ipaddress==1.0.23
-      - ipython==8.18.1
-      - isort==5.13.2
-      - jedi==0.19.1
-      - jinja2==3.1.4
-      - jsonargparse==4.14.1
-      - keras==3.3.3
-      - kiwisolver==1.4.5
-      - lazy-loader==0.4
-      - libclang==18.1.1
-      - lightning-utilities==0.11.2
-      - llvmlite==0.43.0
-      - lmdb==1.5.1
-      - lpips==0.1.4
-      - markdown==3.6
-      - markdown-it-py==3.0.0
-      - markupsafe==2.1.5
-      - matplotlib==3.9.0
-      - matplotlib-inline==0.1.7
-      - mccabe==0.7.0
-      - mdurl==0.1.2
-      - mediapy==1.2.0
-      - ml-dtypes==0.3.2
-      - moviepy==1.0.3
-      - mpmath==1.3.0
-      - msgpack==1.0.8
-      - mypy-extensions==1.0.0
-      - namex==0.0.8
-      - networkx==3.2.1
-      - nodeenv==1.9.1
-      - numba==0.60.0
-      - numpy==1.24.4
-      - oauthlib==3.2.2
-      - omegaconf==2.3.0
-      - openai-clip==1.0.1
-      - opencv-python==4.9.0.80
-      - opencv-python-headless==4.10.0.84
-      - opt-einsum==3.3.0
-      - optree==0.11.0
-      - ordered-set==4.1.0
-      - packaging==22.0
-      - pandas==1.5.3
-      - parameterized==0.9.0
-      - parso==0.8.4
-      - pastedeploy==3.1.0
-      - pathspec==0.12.1
-      - pathtools==0.1.2
-      - pbkdf2==1.3
-      - pexpect==4.9.0
-      - pillow==10.3.0
-      - plaster==1.1.2
-      - plaster-pastedeploy==1.0.1
-      - platformdirs==4.2.2
-      - pluggy==1.5.0
-      - proglog==0.1.10
-      - promise==2.3
-      - prompt-toolkit==3.0.47
-      - protobuf==3.20.3
-      - psutil==6.0.0
-      - ptyprocess==0.7.0
-      - pure-eval==0.2.2
-      - pyarrow==11.0.0
-      - pycocotools==2.0.7
-      - pycodestyle==2.12.0
-      - pycparser==2.22
-      - pydantic==1.10.17
-      - pyflakes==3.2.0
-      - pygments==2.18.0
-      - pyiqa==0.1.13
-      - pyjwt==2.8.0
-      - pyopenssl==23.2.0
-      - pyparsing==3.1.2
-      - pyramid==2.0.2
-      - pyramid-mailer==0.15.1
-      - pysocks==1.7.1
-      - pytest==8.3.3
-      - python-dateutil==2.9.0.post0
-      - python-etcd==0.4.5
-      - python3-openid==3.2.0
-      - pytz==2024.1
-      - pyyaml==6.0.1
-      - regex==2024.5.15
-      - repoze-sendmail==4.4.1
-      - requests==2.32.3
-      - requests-oauthlib==2.0.0
-      - rich==13.7.1
-      - rotary-embedding-torch==0.5.3
-      - safetensors==0.4.3
-      - scenedetect==0.6.4
-      - schedule==1.2.2
-      - scikit-image==0.24.0
-      - scipy==1.13.1
-      - sentencepiece==0.2.0
-      - sentry-sdk==2.6.0
-      - setproctitle==1.3.3
-      - shortuuid==1.0.13
-      - six==1.16.0
-      - smmap==5.0.1
-      - sortedcontainers==2.4.0
-      - soupsieve==2.5
-      - sqlalchemy==2.0.31
-      - stack-data==0.6.3
-      - sympy==1.12.1
-      - tabulate==0.9.0
-      - tb-nightly==2.20.0a20250528
-      - tenacity==8.4.1
-      - tensorboard==2.16.2
-      - tensorboard-data-server==0.7.2
-      - tensorflow==2.16.1
-      - tensorflow-io-gcs-filesystem==0.37.0
-      - termcolor==2.4.0
-      - tifffile==2024.8.30
-      - tiktoken==0.9.0
-      - timm==1.0.11
-      - tokenizers==0.20.3
-      - tomli==2.0.1
-      - torch==2.4.0+cu121
-      - torch-fidelity==0.3.0
-      - torchaudio==2.4.0+cu121
-      - torchmetrics==1.3.2
-      - torchvision==0.19.0+cu121
-      - tqdm==4.66.4
-      - traitlets==5.14.3
-      - transaction==4.0
-      - transformers==4.46.2
-      - transformers-stream-generator==0.0.5
-      - translationstring==1.4
-      - triton==3.0.0
-      - typing-extensions==4.12.2
-      - urllib3==1.26.19
-      - velruse==1.1.1
-      - venusian==3.1.0
-      - virtualenv==20.26.3
-      - wcwidth==0.2.13
-      - webob==1.8.7
-      - werkzeug==3.0.3
-      - wrapt==1.16.0
-      - wtforms==3.1.2
-      - wtforms-recaptcha==0.3.2
-      - yapf==0.43.0
-      - zipp==3.19.2
-      - zope-deprecation==5.0
-      - zope-interface==6.4.post2
-      - zope-sqlalchemy==3.1

modelsx/dit/attention.py

@@ -1,46 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-import torch
-import torch.nn.functional as F
-
-from flash_attn import flash_attn_varlen_func
-
-from torch import nn
-
-class TorchAttention(nn.Module):
-    def tflops(self, args, kwargs, output) -> float:
-        assert len(args) == 0 or len(args) > 2, "query, key should both provided by args / kwargs"
-        q = kwargs.get("query") or args[0]
-        k = kwargs.get("key") or args[1]
-        b, h, sq, d = q.shape
-        b, h, sk, d = k.shape
-        return b * h * (4 * d * (sq / 1e6) * (sk / 1e6))
-
-    def forward(self, *args, **kwargs):
-        return F.scaled_dot_product_attention(*args, **kwargs)
-
-
-class FlashAttentionVarlen(nn.Module):
-    def tflops(self, args, kwargs, output) -> float:
-        cu_seqlens_q = kwargs["cu_seqlens_q"]
-        cu_seqlens_k = kwargs["cu_seqlens_k"]
-        _, h, d = output.shape
-        seqlens_q = (cu_seqlens_q[1:] - cu_seqlens_q[:-1]) / 1e6
-        seqlens_k = (cu_seqlens_k[1:] - cu_seqlens_k[:-1]) / 1e6
-        return h * (4 * d * (seqlens_q * seqlens_k).sum())
-
-    def forward(self, *args, **kwargs):
-        kwargs["deterministic"] = torch.are_deterministic_algorithms_enabled()
-        return flash_attn_varlen_func(*args, **kwargs)

modelsx/dit/blocks/__init__.py

@@ -1,25 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from .mmdit_window_block import MMWindowTransformerBlock
-
-dit_blocks = {
-    "mmdit_window": MMWindowTransformerBlock,
-}
-
-
-def get_block(block_type: str):
-    if block_type in dit_blocks:
-        return dit_blocks[block_type]
-    raise NotImplementedError(f"{block_type} is not supported")

modelsx/dit/blocks/mmdit_window_block.py

@@ -1,233 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Tuple, Union
-import torch
-from einops import rearrange
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.modules.utils import _triple
-
-from common.distributed.ops import (
-    gather_heads,
-    gather_heads_scatter_seq,
-    gather_seq_scatter_heads_qkv,
-    scatter_heads,
-)
-
-from ..attention import TorchAttention
-from ..mlp import get_mlp
-from ..mm import MMArg, MMModule
-from ..modulation import ada_layer_type
-from ..normalization import norm_layer_type
-from ..rope import RotaryEmbedding3d
-
-
-class MMWindowAttention(nn.Module):
-    def __init__(
-        self,
-        vid_dim: int,
-        txt_dim: int,
-        heads: int,
-        head_dim: int,
-        qk_bias: bool,
-        qk_rope: bool,
-        qk_norm: norm_layer_type,
-        qk_norm_eps: float,
-        window: Union[int, Tuple[int, int, int]],
-        window_method: str,
-        shared_qkv: bool,
-    ):
-        super().__init__()
-        dim = MMArg(vid_dim, txt_dim)
-        inner_dim = heads * head_dim
-        qkv_dim = inner_dim * 3
-
-        self.window = _triple(window)
-        self.window_method = window_method
-        assert all(map(lambda v: isinstance(v, int) and v >= 0, self.window))
-
-        self.head_dim = head_dim
-        self.proj_qkv = MMModule(nn.Linear, dim, qkv_dim, bias=qk_bias, shared_weights=shared_qkv)
-        self.proj_out = MMModule(nn.Linear, inner_dim, dim, shared_weights=shared_qkv)
-        self.norm_q = MMModule(qk_norm, dim=head_dim, eps=qk_norm_eps, elementwise_affine=True)
-        self.norm_k = MMModule(qk_norm, dim=head_dim, eps=qk_norm_eps, elementwise_affine=True)
-        self.rope = RotaryEmbedding3d(dim=head_dim // 2) if qk_rope else None
-        self.attn = TorchAttention()
-
-    def forward(
-        self,
-        vid: torch.FloatTensor,  # b T H W c
-        txt: torch.FloatTensor,  # b L c
-        txt_mask: torch.BoolTensor,  # b L
-    ) -> Tuple[
-        torch.FloatTensor,
-        torch.FloatTensor,
-    ]:
-        # Project q, k, v.
-        vid_qkv, txt_qkv = self.proj_qkv(vid, txt)
-        vid_qkv = gather_seq_scatter_heads_qkv(vid_qkv, seq_dim=2)
-        _, T, H, W, _ = vid_qkv.shape
-        _, L, _ = txt.shape
-
-        if self.window_method == "win":
-            nt, nh, nw = self.window
-            tt, hh, ww = T // nt, H // nh, W // nw
-        elif self.window_method == "win_by_size":
-            tt, hh, ww = self.window
-            tt, hh, ww = (
-                tt if tt > 0 else T,
-                hh if hh > 0 else H,
-                ww if ww > 0 else W,
-            )
-            nt, nh, nw = T // tt, H // hh, W // ww
-        else:
-            raise NotImplementedError
-
-        vid_qkv = rearrange(vid_qkv, "b T H W (o h d) -> o b h (T H W) d", o=3, d=self.head_dim)
-        txt_qkv = rearrange(txt_qkv, "b L (o h d) -> o b h L d", o=3, d=self.head_dim)
-        txt_qkv = scatter_heads(txt_qkv, dim=2)
-
-        vid_q, vid_k, vid_v = vid_qkv.unbind()
-        txt_q, txt_k, txt_v = txt_qkv.unbind()
-
-        vid_q, txt_q = self.norm_q(vid_q, txt_q)
-        vid_k, txt_k = self.norm_k(vid_k, txt_k)
-
-        if self.rope:
-            vid_q, vid_k = self.rope(vid_q, vid_k, (T, H, W))
-
-        def vid_window(v):
-            return rearrange(
-                v,
-                "b h (nt tt nh hh nw ww) d -> b h (nt nh nw) (tt hh ww) d",
-                hh=hh,
-                ww=ww,
-                tt=tt,
-                nh=nh,
-                nw=nw,
-                nt=nt,
-            )
-
-        def txt_window(t):
-            return rearrange(t, "b h L d -> b h 1 L d").expand(-1, -1, nt * nh * nw, -1, -1)
-
-        # Process video attention.
-        vid_msk = F.pad(txt_mask, (tt * hh * ww, 0), value=True)
-        vid_msk = rearrange(vid_msk, "b l -> b 1 1 1 l").expand(-1, 1, 1, tt * hh * ww, -1)
-        vid_out = self.attn(
-            vid_window(vid_q),
-            torch.cat([vid_window(vid_k), txt_window(txt_k)], dim=-2),
-            torch.cat([vid_window(vid_v), txt_window(txt_v)], dim=-2),
-            vid_msk,
-        )
-        vid_out = rearrange(
-            vid_out,
-            "b h (nt nh nw) (tt hh ww) d -> b (nt tt) (nh hh) (nw ww) (h d)",
-            hh=hh,
-            ww=ww,
-            tt=tt,
-            nh=nh,
-            nw=nw,
-        )
-        vid_out = gather_heads_scatter_seq(vid_out, head_dim=4, seq_dim=2)
-
-        # Process text attention.
-        txt_msk = F.pad(txt_mask, (T * H * W, 0), value=True)
-        txt_msk = rearrange(txt_msk, "b l -> b 1 1 l").expand(-1, 1, L, -1)
-        txt_out = self.attn(
-            txt_q,
-            torch.cat([vid_k, txt_k], dim=-2),
-            torch.cat([vid_v, txt_v], dim=-2),
-            txt_msk,
-        )
-        txt_out = rearrange(txt_out, "b h L d -> b L (h d)")
-        txt_out = gather_heads(txt_out, dim=2)
-
-        # Project output.
-        vid_out, txt_out = self.proj_out(vid_out, txt_out)
-        return vid_out, txt_out
-
-
-class MMWindowTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        *,
-        vid_dim: int,
-        txt_dim: int,
-        emb_dim: int,
-        heads: int,
-        head_dim: int,
-        expand_ratio: int,
-        norm: norm_layer_type,
-        norm_eps: float,
-        ada: ada_layer_type,
-        qk_bias: bool,
-        qk_rope: bool,
-        qk_norm: norm_layer_type,
-        window: Union[int, Tuple[int, int, int]],
-        window_method: str,
-        shared_qkv: bool,
-        shared_mlp: bool,
-        mlp_type: str,
-        **kwargs,
-    ):
-        super().__init__()
-        dim = MMArg(vid_dim, txt_dim)
-        self.attn_norm = MMModule(norm, dim=dim, eps=norm_eps, elementwise_affine=False)
-        self.attn = MMWindowAttention(
-            vid_dim=vid_dim,
-            txt_dim=txt_dim,
-            heads=heads,
-            head_dim=head_dim,
-            qk_bias=qk_bias,
-            qk_rope=qk_rope,
-            qk_norm=qk_norm,
-            qk_norm_eps=norm_eps,
-            window=window,
-            window_method=window_method,
-            shared_qkv=shared_qkv,
-        )
-        self.mlp_norm = MMModule(norm, dim=dim, eps=norm_eps, elementwise_affine=False)
-        self.mlp = MMModule(
-            get_mlp(mlp_type),
-            dim=dim,
-            expand_ratio=expand_ratio,
-            shared_weights=shared_mlp,
-        )
-        self.ada = MMModule(ada, dim=dim, emb_dim=emb_dim, layers=["attn", "mlp"])
-
-    def forward(
-        self,
-        vid: torch.FloatTensor,
-        txt: torch.FloatTensor,
-        txt_mask: torch.BoolTensor,
-        emb: torch.FloatTensor,
-    ) -> Tuple[
-        torch.FloatTensor,
-        torch.FloatTensor,
-    ]:
-        vid_attn, txt_attn = self.attn_norm(vid, txt)
-        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, emb=emb, layer="attn", mode="in")
-        vid_attn, txt_attn = self.attn(vid_attn, txt_attn, txt_mask=txt_mask)
-        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, emb=emb, layer="attn", mode="out")
-        vid_attn, txt_attn = (vid_attn + vid), (txt_attn + txt)
-
-        vid_mlp, txt_mlp = self.mlp_norm(vid_attn, txt_attn)
-        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, emb=emb, layer="mlp", mode="in")
-        vid_mlp, txt_mlp = self.mlp(vid_mlp, txt_mlp)
-        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, emb=emb, layer="mlp", mode="out")
-        vid_mlp, txt_mlp = (vid_mlp + vid_attn), (txt_mlp + txt_attn)
-
-        return vid_mlp, txt_mlp

modelsx/dit/embedding.py

@@ -1,62 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Optional, Union
-import torch
-from diffusers.models.embeddings import get_timestep_embedding
-from torch import nn
-
-
-def emb_add(emb1: torch.Tensor, emb2: Optional[torch.Tensor]):
-    return emb1 if emb2 is None else emb1 + emb2
-
-
-class TimeEmbedding(nn.Module):
-    def __init__(
-        self,
-        sinusoidal_dim: int,
-        hidden_dim: int,
-        output_dim: int,
-    ):
-        super().__init__()
-        self.sinusoidal_dim = sinusoidal_dim
-        self.proj_in = nn.Linear(sinusoidal_dim, hidden_dim)
-        self.proj_hid = nn.Linear(hidden_dim, hidden_dim)
-        self.proj_out = nn.Linear(hidden_dim, output_dim)
-        self.act = nn.SiLU()
-
-    def forward(
-        self,
-        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],
-        device: torch.device,
-        dtype: torch.dtype,
-    ) -> torch.FloatTensor:
-        if not torch.is_tensor(timestep):
-            timestep = torch.tensor([timestep], device=device, dtype=dtype)
-        if timestep.ndim == 0:
-            timestep = timestep[None]
-
-        emb = get_timestep_embedding(
-            timesteps=timestep,
-            embedding_dim=self.sinusoidal_dim,
-            flip_sin_to_cos=False,
-            downscale_freq_shift=0,
-        )
-        emb = emb.to(dtype)
-        emb = self.proj_in(emb)
-        emb = self.act(emb)
-        emb = self.proj_hid(emb)
-        emb = self.act(emb)
-        emb = self.proj_out(emb)
-        return emb

modelsx/dit/mlp.py

@@ -1,62 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Optional
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-def get_mlp(mlp_type: Optional[str] = "normal"):
-    if mlp_type == "normal":
-        return MLP
-    elif mlp_type == "swiglu":
-        return SwiGLUMLP
-
-
-class MLP(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        expand_ratio: int,
-    ):
-        super().__init__()
-        self.proj_in = nn.Linear(dim, dim * expand_ratio)
-        self.act = nn.GELU("tanh")
-        self.proj_out = nn.Linear(dim * expand_ratio, dim)
-
-    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
-        x = self.proj_in(x)
-        x = self.act(x)
-        x = self.proj_out(x)
-        return x
-
-
-class SwiGLUMLP(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        expand_ratio: int,
-        multiple_of: int = 256,
-    ):
-        super().__init__()
-        hidden_dim = int(2 * dim * expand_ratio / 3)
-        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-        self.proj_in_gate = nn.Linear(dim, hidden_dim, bias=False)
-        self.proj_out = nn.Linear(hidden_dim, dim, bias=False)
-        self.proj_in = nn.Linear(dim, hidden_dim, bias=False)
-
-    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
-        x = self.proj_out(F.silu(self.proj_in_gate(x)) * self.proj_in(x))
-        return x

modelsx/dit/mm.py

@@ -1,67 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, Tuple
-import torch
-from torch import nn
-
-
-@dataclass
-class MMArg:
-    vid: Any
-    txt: Any
-
-
-def get_args(key: str, args: List[Any]) -> List[Any]:
-    return [getattr(v, key) if isinstance(v, MMArg) else v for v in args]
-
-
-def get_kwargs(key: str, kwargs: Dict[str, Any]) -> Dict[str, Any]:
-    return {k: getattr(v, key) if isinstance(v, MMArg) else v for k, v in kwargs.items()}
-
-
-class MMModule(nn.Module):
-    def __init__(
-        self,
-        module: Callable[..., nn.Module],
-        *args,
-        shared_weights: bool = False,
-        **kwargs,
-    ):
-        super().__init__()
-        self.shared_weights = shared_weights
-        if self.shared_weights:
-            assert get_args("vid", args) == get_args("txt", args)
-            assert get_kwargs("vid", kwargs) == get_kwargs("txt", kwargs)
-            self.all = module(*get_args("vid", args), **get_kwargs("vid", kwargs))
-        else:
-            self.vid = module(*get_args("vid", args), **get_kwargs("vid", kwargs))
-            self.txt = module(*get_args("txt", args), **get_kwargs("txt", kwargs))
-
-    def forward(
-        self,
-        vid: torch.FloatTensor,
-        txt: torch.FloatTensor,
-        *args,
-        **kwargs,
-    ) -> Tuple[
-        torch.FloatTensor,
-        torch.FloatTensor,
-    ]:
-        vid_module = self.vid if not self.shared_weights else self.all
-        txt_module = self.txt if not self.shared_weights else self.all
-        vid = vid_module(vid, *get_args("vid", args), **get_kwargs("vid", kwargs))
-        txt = txt_module(txt, *get_args("txt", args), **get_kwargs("txt", kwargs))
-        return vid, txt

modelsx/dit/modulation.py

@@ -1,97 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Callable, List, Optional
-import torch
-from einops import rearrange
-from torch import nn
-
-from common.cache import Cache
-from common.distributed.ops import slice_inputs
-
-# (dim: int, emb_dim: int)
-ada_layer_type = Callable[[int, int], nn.Module]
-
-
-def get_ada_layer(ada_layer: str) -> ada_layer_type:
-    if ada_layer == "single":
-        return AdaSingle
-    raise NotImplementedError(f"{ada_layer} is not supported")
-
-
-def expand_dims(x: torch.Tensor, dim: int, ndim: int):
-    """
-    Expand tensor "x" to "ndim" by adding empty dims at "dim".
-    Example: x is (b d), target ndim is 5, add dim at 1, return (b 1 1 1 d).
-    """
-    shape = x.shape
-    shape = shape[:dim] + (1,) * (ndim - len(shape)) + shape[dim:]
-    return x.reshape(shape)
-
-
-class AdaSingle(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        emb_dim: int,
-        layers: List[str],
-    ):
-        assert emb_dim == 6 * dim, "AdaSingle requires emb_dim == 6 * dim"
-        super().__init__()
-        self.dim = dim
-        self.emb_dim = emb_dim
-        self.layers = layers
-        for l in layers:
-            self.register_parameter(f"{l}_shift", nn.Parameter(torch.randn(dim) / dim**0.5))
-            self.register_parameter(f"{l}_scale", nn.Parameter(torch.randn(dim) / dim**0.5 + 1))
-            self.register_parameter(f"{l}_gate", nn.Parameter(torch.randn(dim) / dim**0.5))
-
-    def forward(
-        self,
-        hid: torch.FloatTensor,  # b ... c
-        emb: torch.FloatTensor,  # b d
-        layer: str,
-        mode: str,
-        cache: Cache = Cache(disable=True),
-        branch_tag: str = "",
-        hid_len: Optional[torch.LongTensor] = None,  # b
-    ) -> torch.FloatTensor:
-        idx = self.layers.index(layer)
-        emb = rearrange(emb, "b (d l g) -> b d l g", l=len(self.layers), g=3)[..., idx, :]
-        emb = expand_dims(emb, 1, hid.ndim + 1)
-
-        if hid_len is not None:
-            emb = cache(
-                f"emb_repeat_{idx}_{branch_tag}",
-                lambda: slice_inputs(
-                    torch.cat([e.repeat(l, *([1] * e.ndim)) for e, l in zip(emb, hid_len)]),
-                    dim=0,
-                ),
-            )
-
-        shiftA, scaleA, gateA = emb.unbind(-1)
-        shiftB, scaleB, gateB = (
-            getattr(self, f"{layer}_shift"),
-            getattr(self, f"{layer}_scale"),
-            getattr(self, f"{layer}_gate"),
-        )
-
-        if mode == "in":
-            return hid.mul_(scaleA + scaleB).add_(shiftA + shiftB)
-        if mode == "out":
-            return hid.mul_(gateA + gateB)
-        raise NotImplementedError
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, emb_dim={self.emb_dim}, layers={self.layers}"

modelsx/dit/na.py

@@ -1,241 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from itertools import chain
-from typing import Callable, Dict, List, Tuple
-import einops
-import torch
-
-
-def flatten(
-    hid: List[torch.FloatTensor],  # List of (*** c)
-) -> Tuple[
-    torch.FloatTensor,  # (L c)
-    torch.LongTensor,  # (b n)
-]:
-    assert len(hid) > 0
-    shape = torch.stack([torch.tensor(x.shape[:-1], device=hid[0].device) for x in hid])
-    hid = torch.cat([x.flatten(0, -2) for x in hid])
-    return hid, shape
-
-
-def unflatten(
-    hid: torch.FloatTensor,  # (L c) or (L ... c)
-    hid_shape: torch.LongTensor,  # (b n)
-) -> List[torch.Tensor]:  # List of (*** c) or (*** ... c)
-    hid_len = hid_shape.prod(-1)
-    hid = hid.split(hid_len.tolist())
-    hid = [x.unflatten(0, s.tolist()) for x, s in zip(hid, hid_shape)]
-    return hid
-
-
-def concat(
-    vid: torch.FloatTensor,  # (VL ... c)
-    txt: torch.FloatTensor,  # (TL ... c)
-    vid_len: torch.LongTensor,  # (b)
-    txt_len: torch.LongTensor,  # (b)
-) -> torch.FloatTensor:  # (L ... c)
-    vid = torch.split(vid, vid_len.tolist())
-    txt = torch.split(txt, txt_len.tolist())
-    return torch.cat(list(chain(*zip(vid, txt))))
-
-
-def concat_idx(
-    vid_len: torch.LongTensor,  # (b)
-    txt_len: torch.LongTensor,  # (b)
-) -> Tuple[
-    Callable,
-    Callable,
-]:
-    device = vid_len.device
-    vid_idx = torch.arange(vid_len.sum(), device=device)
-    txt_idx = torch.arange(len(vid_idx), len(vid_idx) + txt_len.sum(), device=device)
-    tgt_idx = concat(vid_idx, txt_idx, vid_len, txt_len)
-    src_idx = torch.argsort(tgt_idx)
-    return (
-        lambda vid, txt: torch.index_select(torch.cat([vid, txt]), 0, tgt_idx),
-        lambda all: torch.index_select(all, 0, src_idx).split([len(vid_idx), len(txt_idx)]),
-    )
-
-
-def unconcat(
-    all: torch.FloatTensor,  # (L ... c)
-    vid_len: torch.LongTensor,  # (b)
-    txt_len: torch.LongTensor,  # (b)
-) -> Tuple[
-    torch.FloatTensor,  # (VL ... c)
-    torch.FloatTensor,  # (TL ... c)
-]:
-    interleave_len = list(chain(*zip(vid_len.tolist(), txt_len.tolist())))
-    all = all.split(interleave_len)
-    vid = torch.cat(all[0::2])
-    txt = torch.cat(all[1::2])
-    return vid, txt
-
-
-def repeat_concat(
-    vid: torch.FloatTensor,  # (VL ... c)
-    txt: torch.FloatTensor,  # (TL ... c)
-    vid_len: torch.LongTensor,  # (n*b)
-    txt_len: torch.LongTensor,  # (b)
-    txt_repeat: List,  # (n)
-) -> torch.FloatTensor:  # (L ... c)
-    vid = torch.split(vid, vid_len.tolist())
-    txt = torch.split(txt, txt_len.tolist())
-    txt = [[x] * n for x, n in zip(txt, txt_repeat)]
-    txt = list(chain(*txt))
-    return torch.cat(list(chain(*zip(vid, txt))))
-
-
-def repeat_concat_idx(
-    vid_len: torch.LongTensor,  # (n*b)
-    txt_len: torch.LongTensor,  # (b)
-    txt_repeat: torch.LongTensor,  # (n)
-) -> Tuple[
-    Callable,
-    Callable,
-]:
-    device = vid_len.device
-    vid_idx = torch.arange(vid_len.sum(), device=device)
-    txt_idx = torch.arange(len(vid_idx), len(vid_idx) + txt_len.sum(), device=device)
-    txt_repeat_list = txt_repeat.tolist()
-    tgt_idx = repeat_concat(vid_idx, txt_idx, vid_len, txt_len, txt_repeat)
-    src_idx = torch.argsort(tgt_idx)
-    txt_idx_len = len(tgt_idx) - len(vid_idx)
-    repeat_txt_len = (txt_len * txt_repeat).tolist()
-
-    def unconcat_coalesce(all):
-        """
-        Un-concat vid & txt, and coalesce the repeated txt.
-        e.g. vid [0 1 2 3 4 5 6 7 8] -> 3 splits -> [0 1 2] [3 4 5] [6 7 8]
-             txt [9 10]
-             repeat_concat ==> [0 1 2 9 10 3 4 5 9 10 6 7 8 9 10]
-             1. argsort re-index ==> [0 1 2 3 4 5 6 7 8 9 9 9 10 10 10]
-                           split ==> vid_out [0 1 2 3 4 5 6 7 8] txt_out [9 9 9 10 10 10]
-             2. reshape & mean for each sample to coalesce the repeated txt.
-        """
-        vid_out, txt_out = all[src_idx].split([len(vid_idx), txt_idx_len])
-        txt_out_coalesced = []
-        for txt, repeat_time in zip(txt_out.split(repeat_txt_len), txt_repeat_list):
-            txt = txt.reshape(-1, repeat_time, *txt.shape[1:]).mean(1)
-            txt_out_coalesced.append(txt)
-        return vid_out, torch.cat(txt_out_coalesced)
-
-    # Note: Backward of torch.index_select is non-deterministic when existing repeated index,
-    # the difference may cumulative like torch.repeat_interleave, so we use vanilla index here.
-    return (
-        lambda vid, txt: torch.cat([vid, txt])[tgt_idx],
-        lambda all: unconcat_coalesce(all),
-    )
-
-
-def rearrange(
-    hid: torch.FloatTensor,  # (L c)
-    hid_shape: torch.LongTensor,  # (b n)
-    pattern: str,
-    **kwargs: Dict[str, int],
-) -> Tuple[
-    torch.FloatTensor,
-    torch.LongTensor,
-]:
-    return flatten([einops.rearrange(h, pattern, **kwargs) for h in unflatten(hid, hid_shape)])
-
-
-def rearrange_idx(
-    hid_shape: torch.LongTensor,  # (b n)
-    pattern: str,
-    **kwargs: Dict[str, int],
-) -> Tuple[Callable, Callable, torch.LongTensor]:
-    hid_idx = torch.arange(hid_shape.prod(-1).sum(), device=hid_shape.device).unsqueeze(-1)
-    tgt_idx, tgt_shape = rearrange(hid_idx, hid_shape, pattern, **kwargs)
-    tgt_idx = tgt_idx.squeeze(-1)
-    src_idx = torch.argsort(tgt_idx)
-    return (
-        lambda hid: torch.index_select(hid, 0, tgt_idx),
-        lambda hid: torch.index_select(hid, 0, src_idx),
-        tgt_shape,
-    )
-
-
-def repeat(
-    hid: torch.FloatTensor,  # (L c)
-    hid_shape: torch.LongTensor,  # (b n)
-    pattern: str,
-    **kwargs: Dict[str, torch.LongTensor],  # (b)
-) -> Tuple[
-    torch.FloatTensor,
-    torch.LongTensor,
-]:
-    hid = unflatten(hid, hid_shape)
-    kwargs = [{k: v[i].item() for k, v in kwargs.items()} for i in range(len(hid))]
-    return flatten([einops.repeat(h, pattern, **a) for h, a in zip(hid, kwargs)])
-
-
-def pack(
-    samples: List[torch.Tensor],  # List of (h w c).
-) -> Tuple[
-    List[torch.Tensor],  # groups [(b1 h1 w1 c1), (b2 h2 w2 c2)]
-    List[List[int]],  # reversal indices.
-]:
-    batches = {}
-    indices = {}
-    for i, sample in enumerate(samples):
-        shape = sample.shape
-        batches[shape] = batches.get(shape, [])
-        indices[shape] = indices.get(shape, [])
-        batches[shape].append(sample)
-        indices[shape].append(i)
-
-    batches = list(map(torch.stack, batches.values()))
-    indices = list(indices.values())
-    return batches, indices
-
-
-def unpack(
-    batches: List[torch.Tensor],
-    indices: List[List[int]],
-) -> List[torch.Tensor]:
-    samples = [None] * (max(chain(*indices)) + 1)
-    for batch, index in zip(batches, indices):
-        for sample, i in zip(batch.unbind(), index):
-            samples[i] = sample
-    return samples
-
-
-def window(
-    hid: torch.FloatTensor,  # (L c)
-    hid_shape: torch.LongTensor,  # (b n)
-    window_fn: Callable[[torch.Tensor], List[torch.Tensor]],
-):
-    hid = unflatten(hid, hid_shape)
-    hid = list(map(window_fn, hid))
-    hid_windows = torch.tensor(list(map(len, hid)), device=hid_shape.device)
-    hid, hid_shape = flatten(list(chain(*hid)))
-    return hid, hid_shape, hid_windows
-
-
-def window_idx(
-    hid_shape: torch.LongTensor,  # (b n)
-    window_fn: Callable[[torch.Tensor], List[torch.Tensor]],
-):
-    hid_idx = torch.arange(hid_shape.prod(-1).sum(), device=hid_shape.device).unsqueeze(-1)
-    tgt_idx, tgt_shape, tgt_windows = window(hid_idx, hid_shape, window_fn)
-    tgt_idx = tgt_idx.squeeze(-1)
-    src_idx = torch.argsort(tgt_idx)
-    return (
-        lambda hid: torch.index_select(hid, 0, tgt_idx),
-        lambda hid: torch.index_select(hid, 0, src_idx),
-        tgt_shape,
-        tgt_windows,
-    )

modelsx/dit/nablocks/__init__.py

@@ -1,25 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from .mmsr_block import NaMMSRTransformerBlock
-
-nadit_blocks = {
-    "mmdit_sr": NaMMSRTransformerBlock,
-}
-
-
-def get_nablock(block_type: str):
-    if block_type in nadit_blocks:
-        return nadit_blocks[block_type]
-    raise NotImplementedError(f"{block_type} is not supported")

modelsx/dit/nablocks/mmsr_block.py

@@ -1,248 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Tuple, Union
-import torch
-from einops import rearrange
-from torch.nn import functional as F
-
-# from ..cache import Cache
-from common.cache import Cache
-from common.distributed.ops import gather_heads_scatter_seq, gather_seq_scatter_heads_qkv
-
-from .. import na
-from ..attention import FlashAttentionVarlen
-from ..blocks.mmdit_window_block import MMWindowAttention, MMWindowTransformerBlock
-from ..mm import MMArg
-from ..modulation import ada_layer_type
-from ..normalization import norm_layer_type
-from ..rope import NaRotaryEmbedding3d
-from ..window import get_window_op
-
-
-class NaSwinAttention(MMWindowAttention):
-    def __init__(
-        self,
-        vid_dim: int,
-        txt_dim: int,
-        heads: int,
-        head_dim: int,
-        qk_bias: bool,
-        qk_rope: bool,
-        qk_norm: norm_layer_type,
-        qk_norm_eps: float,
-        window: Union[int, Tuple[int, int, int]],
-        window_method: str,
-        shared_qkv: bool,
-        **kwargs,
-    ):
-        super().__init__(
-            vid_dim=vid_dim,
-            txt_dim=txt_dim,
-            heads=heads,
-            head_dim=head_dim,
-            qk_bias=qk_bias,
-            qk_rope=qk_rope,
-            qk_norm=qk_norm,
-            qk_norm_eps=qk_norm_eps,
-            window=window,
-            window_method=window_method,
-            shared_qkv=shared_qkv,
-        )
-        self.rope = NaRotaryEmbedding3d(dim=head_dim // 2) if qk_rope else None
-        self.attn = FlashAttentionVarlen()
-        self.window_op = get_window_op(window_method)
-
-    def forward(
-        self,
-        vid: torch.FloatTensor,  # l c
-        txt: torch.FloatTensor,  # l c
-        vid_shape: torch.LongTensor,  # b 3
-        txt_shape: torch.LongTensor,  # b 1
-        cache: Cache,
-    ) -> Tuple[
-        torch.FloatTensor,
-        torch.FloatTensor,
-    ]:
-
-        vid_qkv, txt_qkv = self.proj_qkv(vid, txt)
-        vid_qkv = gather_seq_scatter_heads_qkv(
-            vid_qkv,
-            seq_dim=0,
-            qkv_shape=vid_shape,
-            cache=cache.namespace("vid"),
-        )
-        txt_qkv = gather_seq_scatter_heads_qkv(
-            txt_qkv,
-            seq_dim=0,
-            qkv_shape=txt_shape,
-            cache=cache.namespace("txt"),
-        )
-
-        # re-org the input seq for window attn
-        cache_win = cache.namespace(f"{self.window_method}_{self.window}_sd3")
-
-        def make_window(x: torch.Tensor):
-            t, h, w, _ = x.shape
-            window_slices = self.window_op((t, h, w), self.window)
-            return [x[st, sh, sw] for (st, sh, sw) in window_slices]
-
-        window_partition, window_reverse, window_shape, window_count = cache_win(
-            "win_transform",
-            lambda: na.window_idx(vid_shape, make_window),
-        )
-        vid_qkv_win = window_partition(vid_qkv)
-
-        vid_qkv_win = rearrange(vid_qkv_win, "l (o h d) -> l o h d", o=3, d=self.head_dim)
-        txt_qkv = rearrange(txt_qkv, "l (o h d) -> l o h d", o=3, d=self.head_dim)
-
-        vid_q, vid_k, vid_v = vid_qkv_win.unbind(1)
-        txt_q, txt_k, txt_v = txt_qkv.unbind(1)
-
-        vid_q, txt_q = self.norm_q(vid_q, txt_q)
-        vid_k, txt_k = self.norm_k(vid_k, txt_k)
-
-        txt_len = cache("txt_len", lambda: txt_shape.prod(-1))
-
-        vid_len_win = cache_win("vid_len", lambda: window_shape.prod(-1))
-        txt_len_win = cache_win("txt_len", lambda: txt_len.repeat_interleave(window_count))
-        all_len_win = cache_win("all_len", lambda: vid_len_win + txt_len_win)
-        concat_win, unconcat_win = cache_win(
-            "mm_pnp", lambda: na.repeat_concat_idx(vid_len_win, txt_len, window_count)
-        )
-
-        # window rope
-        if self.rope:
-            vid_q, vid_k = self.rope(vid_q, vid_k, window_shape, cache_win)
-
-        out = self.attn(
-            q=concat_win(vid_q, txt_q).bfloat16(),
-            k=concat_win(vid_k, txt_k).bfloat16(),
-            v=concat_win(vid_v, txt_v).bfloat16(),
-            cu_seqlens_q=cache_win(
-                "vid_seqlens_q", lambda: F.pad(all_len_win.cumsum(0), (1, 0)).int()
-            ),
-            cu_seqlens_k=cache_win(
-                "vid_seqlens_k", lambda: F.pad(all_len_win.cumsum(0), (1, 0)).int()
-            ),
-            max_seqlen_q=cache_win("vid_max_seqlen_q", lambda: all_len_win.max().item()),
-            max_seqlen_k=cache_win("vid_max_seqlen_k", lambda: all_len_win.max().item()),
-        ).type_as(vid_q)
-
-        # text pooling
-        vid_out, txt_out = unconcat_win(out)
-
-        vid_out = rearrange(vid_out, "l h d -> l (h d)")
-        txt_out = rearrange(txt_out, "l h d -> l (h d)")
-        vid_out = window_reverse(vid_out)
-
-        vid_out = gather_heads_scatter_seq(vid_out, head_dim=1, seq_dim=0)
-        txt_out = gather_heads_scatter_seq(txt_out, head_dim=1, seq_dim=0)
-
-        vid_out, txt_out = self.proj_out(vid_out, txt_out)
-
-        return vid_out, txt_out
-
-
-class NaMMSRTransformerBlock(MMWindowTransformerBlock):
-    def __init__(
-        self,
-        *,
-        vid_dim: int,
-        txt_dim: int,
-        emb_dim: int,
-        heads: int,
-        head_dim: int,
-        expand_ratio: int,
-        norm: norm_layer_type,
-        norm_eps: float,
-        ada: ada_layer_type,
-        qk_bias: bool,
-        qk_rope: bool,
-        qk_norm: norm_layer_type,
-        shared_qkv: bool,
-        shared_mlp: bool,
-        mlp_type: str,
-        **kwargs,
-    ):
-        super().__init__(
-            vid_dim=vid_dim,
-            txt_dim=txt_dim,
-            emb_dim=emb_dim,
-            heads=heads,
-            head_dim=head_dim,
-            expand_ratio=expand_ratio,
-            norm=norm,
-            norm_eps=norm_eps,
-            ada=ada,
-            qk_bias=qk_bias,
-            qk_rope=qk_rope,
-            qk_norm=qk_norm,
-            shared_qkv=shared_qkv,
-            shared_mlp=shared_mlp,
-            mlp_type=mlp_type,
-            **kwargs,
-        )
-
-        self.attn = NaSwinAttention(
-            vid_dim=vid_dim,
-            txt_dim=txt_dim,
-            heads=heads,
-            head_dim=head_dim,
-            qk_bias=qk_bias,
-            qk_rope=qk_rope,
-            qk_norm=qk_norm,
-            qk_norm_eps=norm_eps,
-            shared_qkv=shared_qkv,
-            **kwargs,
-        )
-
-    def forward(
-        self,
-        vid: torch.FloatTensor,  # l c
-        txt: torch.FloatTensor,  # l c
-        vid_shape: torch.LongTensor,  # b 3
-        txt_shape: torch.LongTensor,  # b 1
-        emb: torch.FloatTensor,
-        cache: Cache,
-    ) -> Tuple[
-        torch.FloatTensor,
-        torch.FloatTensor,
-        torch.LongTensor,
-        torch.LongTensor,
-    ]:
-        hid_len = MMArg(
-            cache("vid_len", lambda: vid_shape.prod(-1)),
-            cache("txt_len", lambda: txt_shape.prod(-1)),
-        )
-        ada_kwargs = {
-            "emb": emb,
-            "hid_len": hid_len,
-            "cache": cache,
-            "branch_tag": MMArg("vid", "txt"),
-        }
-
-        vid_attn, txt_attn = self.attn_norm(vid, txt)
-        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, layer="attn", mode="in", **ada_kwargs)
-        vid_attn, txt_attn = self.attn(vid_attn, txt_attn, vid_shape, txt_shape, cache)
-        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, layer="attn", mode="out", **ada_kwargs)
-        vid_attn, txt_attn = (vid_attn + vid), (txt_attn + txt)
-
-        vid_mlp, txt_mlp = self.mlp_norm(vid_attn, txt_attn)
-        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, layer="mlp", mode="in", **ada_kwargs)
-        vid_mlp, txt_mlp = self.mlp(vid_mlp, txt_mlp)
-        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, layer="mlp", mode="out", **ada_kwargs)
-        vid_mlp, txt_mlp = (vid_mlp + vid_attn), (txt_mlp + txt_attn)
-
-        return vid_mlp, txt_mlp, vid_shape, txt_shape

modelsx/dit/nadit.py

@@ -1,350 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union, Callable
-import torch
-from torch import nn
-
-from common.cache import Cache
-from common.distributed.ops import slice_inputs
-
-from . import na
-from .embedding import TimeEmbedding
-from .modulation import get_ada_layer
-from .nablocks import get_nablock
-from .normalization import get_norm_layer
-from .patch import NaPatchIn, NaPatchOut
-
-# Fake func, no checkpointing is required for inference
-def gradient_checkpointing(module: Union[Callable, nn.Module], *args, enabled: bool, **kwargs):
-    return module(*args, **kwargs)
-
-@dataclass
-class NaDiTOutput:
-    vid_sample: torch.Tensor
-
-
-class NaDiT(nn.Module):
-    """
-    Native Resolution Diffusion Transformer (NaDiT)
-    """
-
-    gradient_checkpointing = False
-
-    def __init__(
-        self,
-        vid_in_channels: int,
-        vid_out_channels: int,
-        vid_dim: int,
-        txt_in_dim: Optional[int],
-        txt_dim: Optional[int],
-        emb_dim: int,
-        heads: int,
-        head_dim: int,
-        expand_ratio: int,
-        norm: Optional[str],
-        norm_eps: float,
-        ada: str,
-        qk_bias: bool,
-        qk_rope: bool,
-        qk_norm: Optional[str],
-        patch_size: Union[int, Tuple[int, int, int]],
-        num_layers: int,
-        block_type: Union[str, Tuple[str]],
-        shared_qkv: bool = False,
-        shared_mlp: bool = False,
-        mlp_type: str = "normal",
-        window: Optional[Tuple] = None,
-        window_method: Optional[Tuple[str]] = None,
-        temporal_window_size: int = None,
-        temporal_shifted: bool = False,
-        **kwargs,
-    ):
-        ada = get_ada_layer(ada)
-        norm = get_norm_layer(norm)
-        qk_norm = get_norm_layer(qk_norm)
-        if isinstance(block_type, str):
-            block_type = [block_type] * num_layers
-        elif len(block_type) != num_layers:
-            raise ValueError("The ``block_type`` list should equal to ``num_layers``.")
-        super().__init__()
-        self.vid_in = NaPatchIn(
-            in_channels=vid_in_channels,
-            patch_size=patch_size,
-            dim=vid_dim,
-        )
-        self.txt_in = (
-            nn.Linear(txt_in_dim, txt_dim)
-            if txt_in_dim and txt_in_dim != txt_dim
-            else nn.Identity()
-        )
-        self.emb_in = TimeEmbedding(
-            sinusoidal_dim=256,
-            hidden_dim=max(vid_dim, txt_dim),
-            output_dim=emb_dim,
-        )
-
-        if window is None or isinstance(window[0], int):
-            window = [window] * num_layers
-        if window_method is None or isinstance(window_method, str):
-            window_method = [window_method] * num_layers
-        if temporal_window_size is None or isinstance(temporal_window_size, int):
-            temporal_window_size = [temporal_window_size] * num_layers
-        if temporal_shifted is None or isinstance(temporal_shifted, bool):
-            temporal_shifted = [temporal_shifted] * num_layers
-
-        self.blocks = nn.ModuleList(
-            [
-                get_nablock(block_type[i])(
-                    vid_dim=vid_dim,
-                    txt_dim=txt_dim,
-                    emb_dim=emb_dim,
-                    heads=heads,
-                    head_dim=head_dim,
-                    expand_ratio=expand_ratio,
-                    norm=norm,
-                    norm_eps=norm_eps,
-                    ada=ada,
-                    qk_bias=qk_bias,
-                    qk_rope=qk_rope,
-                    qk_norm=qk_norm,
-                    shared_qkv=shared_qkv,
-                    shared_mlp=shared_mlp,
-                    mlp_type=mlp_type,
-                    window=window[i],
-                    window_method=window_method[i],
-                    temporal_window_size=temporal_window_size[i],
-                    temporal_shifted=temporal_shifted[i],
-                    **kwargs,
-                )
-                for i in range(num_layers)
-            ]
-        )
-        self.vid_out = NaPatchOut(
-            out_channels=vid_out_channels,
-            patch_size=patch_size,
-            dim=vid_dim,
-        )
-
-        self.need_txt_repeat = block_type[0] in [
-            "mmdit_stwin",
-            "mmdit_stwin_spatial",
-            "mmdit_stwin_3d_spatial",
-        ]
-
-    def set_gradient_checkpointing(self, enable: bool):
-        self.gradient_checkpointing = enable
-
-    def forward(
-        self,
-        vid: torch.FloatTensor,  # l c
-        txt: torch.FloatTensor,  # l c
-        vid_shape: torch.LongTensor,  # b 3
-        txt_shape: torch.LongTensor,  # b 1
-        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],  # b
-        disable_cache: bool = True,  # for test
-    ):
-        # Text input.
-        if txt_shape.size(-1) == 1 and self.need_txt_repeat:
-            txt, txt_shape = na.repeat(txt, txt_shape, "l c -> t l c", t=vid_shape[:, 0])
-        # slice vid after patching in when using sequence parallelism
-        txt = slice_inputs(txt, dim=0)
-        txt = self.txt_in(txt)
-
-        # Video input.
-        # Sequence parallel slicing is done inside patching class.
-        vid, vid_shape = self.vid_in(vid, vid_shape)
-
-        # Embedding input.
-        emb = self.emb_in(timestep, device=vid.device, dtype=vid.dtype)
-
-        # Body
-        cache = Cache(disable=disable_cache)
-        for i, block in enumerate(self.blocks):
-            vid, txt, vid_shape, txt_shape = gradient_checkpointing(
-                enabled=(self.gradient_checkpointing and self.training),
-                module=block,
-                vid=vid,
-                txt=txt,
-                vid_shape=vid_shape,
-                txt_shape=txt_shape,
-                emb=emb,
-                cache=cache,
-            )
-
-        vid, vid_shape = self.vid_out(vid, vid_shape, cache)
-        return NaDiTOutput(vid_sample=vid)
-
-
-class NaDiTUpscaler(nn.Module):
-    """
-    Native Resolution Diffusion Transformer (NaDiT)
-    """
-
-    gradient_checkpointing = False
-
-    def __init__(
-        self,
-        vid_in_channels: int,
-        vid_out_channels: int,
-        vid_dim: int,
-        txt_in_dim: Optional[int],
-        txt_dim: Optional[int],
-        emb_dim: int,
-        heads: int,
-        head_dim: int,
-        expand_ratio: int,
-        norm: Optional[str],
-        norm_eps: float,
-        ada: str,
-        qk_bias: bool,
-        qk_rope: bool,
-        qk_norm: Optional[str],
-        patch_size: Union[int, Tuple[int, int, int]],
-        num_layers: int,
-        block_type: Union[str, Tuple[str]],
-        shared_qkv: bool = False,
-        shared_mlp: bool = False,
-        mlp_type: str = "normal",
-        window: Optional[Tuple] = None,
-        window_method: Optional[Tuple[str]] = None,
-        temporal_window_size: int = None,
-        temporal_shifted: bool = False,
-        **kwargs,
-    ):
-        ada = get_ada_layer(ada)
-        norm = get_norm_layer(norm)
-        qk_norm = get_norm_layer(qk_norm)
-        if isinstance(block_type, str):
-            block_type = [block_type] * num_layers
-        elif len(block_type) != num_layers:
-            raise ValueError("The ``block_type`` list should equal to ``num_layers``.")
-        super().__init__()
-        self.vid_in = NaPatchIn(
-            in_channels=vid_in_channels,
-            patch_size=patch_size,
-            dim=vid_dim,
-        )
-        self.txt_in = (
-            nn.Linear(txt_in_dim, txt_dim)
-            if txt_in_dim and txt_in_dim != txt_dim
-            else nn.Identity()
-        )
-        self.emb_in = TimeEmbedding(
-            sinusoidal_dim=256,
-            hidden_dim=max(vid_dim, txt_dim),
-            output_dim=emb_dim,
-        )
-
-        self.emb_scale = TimeEmbedding(
-            sinusoidal_dim=256,
-            hidden_dim=max(vid_dim, txt_dim),
-            output_dim=emb_dim,
-        )
-
-        if window is None or isinstance(window[0], int):
-            window = [window] * num_layers
-        if window_method is None or isinstance(window_method, str):
-            window_method = [window_method] * num_layers
-        if temporal_window_size is None or isinstance(temporal_window_size, int):
-            temporal_window_size = [temporal_window_size] * num_layers
-        if temporal_shifted is None or isinstance(temporal_shifted, bool):
-            temporal_shifted = [temporal_shifted] * num_layers
-
-        self.blocks = nn.ModuleList(
-            [
-                get_nablock(block_type[i])(
-                    vid_dim=vid_dim,
-                    txt_dim=txt_dim,
-                    emb_dim=emb_dim,
-                    heads=heads,
-                    head_dim=head_dim,
-                    expand_ratio=expand_ratio,
-                    norm=norm,
-                    norm_eps=norm_eps,
-                    ada=ada,
-                    qk_bias=qk_bias,
-                    qk_rope=qk_rope,
-                    qk_norm=qk_norm,
-                    shared_qkv=shared_qkv,
-                    shared_mlp=shared_mlp,
-                    mlp_type=mlp_type,
-                    window=window[i],
-                    window_method=window_method[i],
-                    temporal_window_size=temporal_window_size[i],
-                    temporal_shifted=temporal_shifted[i],
-                    **kwargs,
-                )
-                for i in range(num_layers)
-            ]
-        )
-        self.vid_out = NaPatchOut(
-            out_channels=vid_out_channels,
-            patch_size=patch_size,
-            dim=vid_dim,
-        )
-
-        self.need_txt_repeat = block_type[0] in [
-            "mmdit_stwin",
-            "mmdit_stwin_spatial",
-            "mmdit_stwin_3d_spatial",
-        ]
-
-    def set_gradient_checkpointing(self, enable: bool):
-        self.gradient_checkpointing = enable
-
-    def forward(
-        self,
-        vid: torch.FloatTensor,  # l c
-        txt: torch.FloatTensor,  # l c
-        vid_shape: torch.LongTensor,  # b 3
-        txt_shape: torch.LongTensor,  # b 1
-        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],  # b
-        downscale: Union[int, float, torch.IntTensor, torch.FloatTensor],  # b
-        disable_cache: bool = False,  # for test
-    ):
-
-        # Text input.
-        if txt_shape.size(-1) == 1 and self.need_txt_repeat:
-            txt, txt_shape = na.repeat(txt, txt_shape, "l c -> t l c", t=vid_shape[:, 0])
-        # slice vid after patching in when using sequence parallelism
-        txt = slice_inputs(txt, dim=0)
-        txt = self.txt_in(txt)
-
-        # Video input.
-        # Sequence parallel slicing is done inside patching class.
-        vid, vid_shape = self.vid_in(vid, vid_shape)
-
-        # Embedding input.
-        emb = self.emb_in(timestep, device=vid.device, dtype=vid.dtype)
-        emb_scale = self.emb_scale(downscale, device=vid.device, dtype=vid.dtype)
-        emb = emb + emb_scale
-
-        # Body
-        cache = Cache(disable=disable_cache)
-        for i, block in enumerate(self.blocks):
-            vid, txt, vid_shape, txt_shape = gradient_checkpointing(
-                enabled=(self.gradient_checkpointing and self.training),
-                module=block,
-                vid=vid,
-                txt=txt,
-                vid_shape=vid_shape,
-                txt_shape=txt_shape,
-                emb=emb,
-                cache=cache,
-            )
-
-        vid, vid_shape = self.vid_out(vid, vid_shape, cache)
-        return NaDiTOutput(vid_sample=vid)

modelsx/dit/normalization.py

@@ -1,63 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Callable, Optional
-from diffusers.models.normalization import RMSNorm
-from torch import nn
-
-# (dim: int, eps: float, elementwise_affine: bool)
-norm_layer_type = Callable[[int, float, bool], nn.Module]
-
-
-def get_norm_layer(norm_type: Optional[str]) -> norm_layer_type:
-
-    def _norm_layer(dim: int, eps: float, elementwise_affine: bool):
-        if norm_type is None:
-            return nn.Identity()
-
-        if norm_type == "layer":
-            return nn.LayerNorm(
-                normalized_shape=dim,
-                eps=eps,
-                elementwise_affine=elementwise_affine,
-            )
-
-        if norm_type == "rms":
-            return RMSNorm(
-                dim=dim,
-                eps=eps,
-                elementwise_affine=elementwise_affine,
-            )
-
-        if norm_type == "fusedln":
-            from apex.normalization import FusedLayerNorm
-
-            return FusedLayerNorm(
-                normalized_shape=dim,
-                elementwise_affine=elementwise_affine,
-                eps=eps,
-            )
-
-        if norm_type == "fusedrms":
-            from apex.normalization import FusedRMSNorm
-
-            return FusedRMSNorm(
-                normalized_shape=dim,
-                elementwise_affine=elementwise_affine,
-                eps=eps,
-            )
-
-        raise NotImplementedError(f"{norm_type} is not supported")
-
-    return _norm_layer

modelsx/dit/patch.py

@@ -1,112 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Tuple, Union
-import torch
-from einops import rearrange
-from torch import nn
-from torch.nn.modules.utils import _triple
-
-from common.cache import Cache
-from common.distributed.ops import gather_outputs, slice_inputs
-
-from . import na
-
-
-class PatchIn(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        patch_size: Union[int, Tuple[int, int, int]],
-        dim: int,
-    ):
-        super().__init__()
-        t, h, w = _triple(patch_size)
-        self.patch_size = t, h, w
-        self.proj = nn.Linear(in_channels * t * h * w, dim)
-
-    def forward(
-        self,
-        vid: torch.Tensor,
-    ) -> torch.Tensor:
-        t, h, w = self.patch_size
-        vid = rearrange(vid, "b c (T t) (H h) (W w) -> b T H W (t h w c)", t=t, h=h, w=w)
-        vid = self.proj(vid)
-        return vid
-
-
-class PatchOut(nn.Module):
-    def __init__(
-        self,
-        out_channels: int,
-        patch_size: Union[int, Tuple[int, int, int]],
-        dim: int,
-    ):
-        super().__init__()
-        t, h, w = _triple(patch_size)
-        self.patch_size = t, h, w
-        self.proj = nn.Linear(dim, out_channels * t * h * w)
-
-    def forward(
-        self,
-        vid: torch.Tensor,
-    ) -> torch.Tensor:
-        t, h, w = self.patch_size
-        vid = self.proj(vid)
-        vid = rearrange(vid, "b T H W (t h w c) -> b c (T t) (H h) (W w)", t=t, h=h, w=w)
-        return vid
-
-
-class NaPatchIn(PatchIn):
-    def forward(
-        self,
-        vid: torch.Tensor,  # l c
-        vid_shape: torch.LongTensor,
-    ) -> torch.Tensor:
-        t, h, w = self.patch_size
-        if not (t == h == w == 1):
-            vid, vid_shape = na.rearrange(
-                vid, vid_shape, "(T t) (H h) (W w) c -> T H W (t h w c)", t=t, h=h, w=w
-            )
-        # slice vid after patching in when using sequence parallelism
-        vid = slice_inputs(vid, dim=0)
-        vid = self.proj(vid)
-        return vid, vid_shape
-
-
-class NaPatchOut(PatchOut):
-    def forward(
-        self,
-        vid: torch.FloatTensor,  # l c
-        vid_shape: torch.LongTensor,
-        cache: Cache = Cache(disable=True),
-    ) -> Tuple[
-        torch.FloatTensor,
-        torch.LongTensor,
-    ]:
-        t, h, w = self.patch_size
-        vid = self.proj(vid)
-        # gather vid before patching out when enabling sequence parallelism
-        vid = gather_outputs(
-            vid,
-            gather_dim=0,
-            padding_dim=0,
-            unpad_shape=vid_shape,
-            cache=cache.namespace("vid"),
-        )
-        if not (t == h == w == 1):
-            vid, vid_shape = na.rearrange(
-                vid, vid_shape, "T H W (t h w c) -> (T t) (H h) (W w) c", t=t, h=h, w=w
-            )
-        return vid, vid_shape

modelsx/dit/rope.py

@@ -1,101 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from functools import lru_cache
-from typing import Tuple
-import torch
-from einops import rearrange
-from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
-from torch import nn
-
-from common.cache import Cache
-
-
-class RotaryEmbeddingBase(nn.Module):
-    def __init__(self, dim: int, rope_dim: int):
-        super().__init__()
-        self.rope = RotaryEmbedding(
-            dim=dim // rope_dim,
-            freqs_for="pixel",
-            max_freq=256,
-        )
-        # 1. Set model.requires_grad_(True) after model creation will make
-        #    the `requires_grad=False` for rope freqs no longer hold.
-        # 2. Even if we don't set requires_grad_(True) explicitly,
-        #    FSDP is not memory efficient when handling fsdp_wrap
-        #    with mixed requires_grad=True/False.
-        # With above consideration, it is easier just remove the freqs
-        # out of nn.Parameters when `learned_freq=False`
-        freqs = self.rope.freqs
-        del self.rope.freqs
-        self.rope.register_buffer("freqs", freqs.data)
-
-    @lru_cache(maxsize=128)
-    def get_axial_freqs(self, *dims):
-        return self.rope.get_axial_freqs(*dims)
-
-
-class RotaryEmbedding3d(RotaryEmbeddingBase):
-    def __init__(self, dim: int):
-        super().__init__(dim, rope_dim=3)
-
-    def forward(
-        self,
-        q: torch.FloatTensor,  # b h l d
-        k: torch.FloatTensor,  # b h l d
-        size: Tuple[int, int, int],
-    ) -> Tuple[
-        torch.FloatTensor,
-        torch.FloatTensor,
-    ]:
-        T, H, W = size
-        freqs = self.get_axial_freqs(T, H, W)
-        q = rearrange(q, "b h (T H W) d -> b h T H W d", T=T, H=H, W=W)
-        k = rearrange(k, "b h (T H W) d -> b h T H W d", T=T, H=H, W=W)
-        q = apply_rotary_emb(freqs, q)
-        k = apply_rotary_emb(freqs, k)
-        q = rearrange(q, "b h T H W d -> b h (T H W) d")
-        k = rearrange(k, "b h T H W d -> b h (T H W) d")
-        return q, k
-
-
-class NaRotaryEmbedding3d(RotaryEmbedding3d):
-    def forward(
-        self,
-        q: torch.FloatTensor,  # L h d
-        k: torch.FloatTensor,  # L h d
-        shape: torch.LongTensor,
-        cache: Cache,
-    ) -> Tuple[
-        torch.FloatTensor,
-        torch.FloatTensor,
-    ]:
-        freqs = cache("rope_freqs_3d", lambda: self.get_freqs(shape))
-        q = rearrange(q, "L h d -> h L d")
-        k = rearrange(k, "L h d -> h L d")
-        q = apply_rotary_emb(freqs, q.float()).to(q.dtype)
-        k = apply_rotary_emb(freqs, k.float()).to(k.dtype)
-        q = rearrange(q, "h L d -> L h d")
-        k = rearrange(k, "h L d -> L h d")
-        return q, k
-
-    def get_freqs(
-        self,
-        shape: torch.LongTensor,
-    ) -> torch.Tensor:
-        freq_list = []
-        for f, h, w in shape.tolist():
-            freqs = self.get_axial_freqs(f, h, w)
-            freq_list.append(freqs.view(-1, freqs.size(-1)))
-        return torch.cat(freq_list, dim=0)

modelsx/dit/window.py

@@ -1,83 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from math import ceil
-from typing import Tuple
-import math
-
-def get_window_op(name: str):
-    if name == "720pwin_by_size_bysize":
-        return make_720Pwindows_bysize
-    if name == "720pswin_by_size_bysize":
-        return make_shifted_720Pwindows_bysize
-    raise ValueError(f"Unknown windowing method: {name}")
-
-
-# -------------------------------- Windowing -------------------------------- #
-def make_720Pwindows_bysize(size: Tuple[int, int, int], num_windows: Tuple[int, int, int]):
-    t, h, w = size
-    resized_nt, resized_nh, resized_nw = num_windows
-    #cal windows under 720p
-    scale = math.sqrt((45 * 80) / (h * w))
-    resized_h, resized_w = round(h * scale), round(w * scale)
-    wh, ww = ceil(resized_h / resized_nh), ceil(resized_w / resized_nw)  # window size.
-    wt = ceil(min(t, 30) / resized_nt)  # window size.
-    nt, nh, nw = ceil(t / wt), ceil(h / wh), ceil(w / ww)  # window size.
-    return [
-        (
-            slice(it * wt, min((it + 1) * wt, t)),
-            slice(ih * wh, min((ih + 1) * wh, h)),
-            slice(iw * ww, min((iw + 1) * ww, w)),
-        )
-        for iw in range(nw)
-        if min((iw + 1) * ww, w) > iw * ww
-        for ih in range(nh)
-        if min((ih + 1) * wh, h) > ih * wh
-        for it in range(nt)
-        if min((it + 1) * wt, t) > it * wt
-    ]
-
-def make_shifted_720Pwindows_bysize(size: Tuple[int, int, int], num_windows: Tuple[int, int, int]):
-    t, h, w = size
-    resized_nt, resized_nh, resized_nw = num_windows
-    #cal windows under 720p
-    scale = math.sqrt((45 * 80) / (h * w))
-    resized_h, resized_w = round(h * scale), round(w * scale)
-    wh, ww = ceil(resized_h / resized_nh), ceil(resized_w / resized_nw)  # window size.
-    wt = ceil(min(t, 30) / resized_nt)  # window size.
-    
-    st, sh, sw = (  # shift size.
-        0.5 if wt < t else 0,
-        0.5 if wh < h else 0,
-        0.5 if ww < w else 0,
-    )
-    nt, nh, nw = ceil((t - st) / wt), ceil((h - sh) / wh), ceil((w - sw) / ww)  # window size.
-    nt, nh, nw = (  # number of window.
-        nt + 1 if st > 0 else 1,
-        nh + 1 if sh > 0 else 1,
-        nw + 1 if sw > 0 else 1,
-    )
-    return [
-        (
-            slice(max(int((it - st) * wt), 0), min(int((it - st + 1) * wt), t)),
-            slice(max(int((ih - sh) * wh), 0), min(int((ih - sh + 1) * wh), h)),
-            slice(max(int((iw - sw) * ww), 0), min(int((iw - sw + 1) * ww), w)),
-        )
-        for iw in range(nw)
-        if min(int((iw - sw + 1) * ww), w) > max(int((iw - sw) * ww), 0)
-        for ih in range(nh)
-        if min(int((ih - sh + 1) * wh), h) > max(int((ih - sh) * wh), 0)
-        for it in range(nt)
-        if min(int((it - st + 1) * wt), t) > max(int((it - st) * wt), 0)
-    ]

modelsx/dit_v2/attention.py

@@ -1,46 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-import torch
-import torch.nn.functional as F
-
-from flash_attn import flash_attn_varlen_func
-
-from torch import nn
-
-class TorchAttention(nn.Module):
-    def tflops(self, args, kwargs, output) -> float:
-        assert len(args) == 0 or len(args) > 2, "query, key should both provided by args / kwargs"
-        q = kwargs.get("query") or args[0]
-        k = kwargs.get("key") or args[1]
-        b, h, sq, d = q.shape
-        b, h, sk, d = k.shape
-        return b * h * (4 * d * (sq / 1e6) * (sk / 1e6))
-
-    def forward(self, *args, **kwargs):
-        return F.scaled_dot_product_attention(*args, **kwargs)
-
-
-class FlashAttentionVarlen(nn.Module):
-    def tflops(self, args, kwargs, output) -> float:
-        cu_seqlens_q = kwargs["cu_seqlens_q"]
-        cu_seqlens_k = kwargs["cu_seqlens_k"]
-        _, h, d = output.shape
-        seqlens_q = (cu_seqlens_q[1:] - cu_seqlens_q[:-1]) / 1e6
-        seqlens_k = (cu_seqlens_k[1:] - cu_seqlens_k[:-1]) / 1e6
-        return h * (4 * d * (seqlens_q * seqlens_k).sum())
-
-    def forward(self, *args, **kwargs):
-        kwargs["deterministic"] = torch.are_deterministic_algorithms_enabled()
-        return flash_attn_varlen_func(*args, **kwargs)

modelsx/dit_v2/embedding.py

@@ -1,62 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Optional, Union
-import torch
-from diffusers.models.embeddings import get_timestep_embedding
-from torch import nn
-
-
-def emb_add(emb1: torch.Tensor, emb2: Optional[torch.Tensor]):
-    return emb1 if emb2 is None else emb1 + emb2
-
-
-class TimeEmbedding(nn.Module):
-    def __init__(
-        self,
-        sinusoidal_dim: int,
-        hidden_dim: int,
-        output_dim: int,
-    ):
-        super().__init__()
-        self.sinusoidal_dim = sinusoidal_dim
-        self.proj_in = nn.Linear(sinusoidal_dim, hidden_dim)
-        self.proj_hid = nn.Linear(hidden_dim, hidden_dim)
-        self.proj_out = nn.Linear(hidden_dim, output_dim)
-        self.act = nn.SiLU()
-
-    def forward(
-        self,
-        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],
-        device: torch.device,
-        dtype: torch.dtype,
-    ) -> torch.FloatTensor:
-        if not torch.is_tensor(timestep):
-            timestep = torch.tensor([timestep], device=device, dtype=dtype)
-        if timestep.ndim == 0:
-            timestep = timestep[None]
-
-        emb = get_timestep_embedding(
-            timesteps=timestep,
-            embedding_dim=self.sinusoidal_dim,
-            flip_sin_to_cos=False,
-            downscale_freq_shift=0,
-        )
-        emb = emb.to(dtype)
-        emb = self.proj_in(emb)
-        emb = self.act(emb)
-        emb = self.proj_hid(emb)
-        emb = self.act(emb)
-        emb = self.proj_out(emb)
-        return emb

modelsx/dit_v2/mlp.py

@@ -1,62 +0,0 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // 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.
-
-from typing import Optional
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-def get_mlp(mlp_type: Optional[str] = "normal"):
-    if mlp_type == "normal":
-        return MLP
-    elif mlp_type == "swiglu":
-        return SwiGLUMLP
-
-
-class MLP(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        expand_ratio: int,
-    ):
-        super().__init__()
-        self.proj_in = nn.Linear(dim, dim * expand_ratio)
-        self.act = nn.GELU("tanh")
-        self.proj_out = nn.Linear(dim * expand_ratio, dim)
-
-    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
-        x = self.proj_in(x)
-        x = self.act(x)
-        x = self.proj_out(x)
-        return x
-
-
-class SwiGLUMLP(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        expand_ratio: int,
-        multiple_of: int = 256,
-    ):
-        super().__init__()
-        hidden_dim = int(2 * dim * expand_ratio / 3)
-        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-        self.proj_in_gate = nn.Linear(dim, hidden_dim, bias=False)
-        self.proj_out = nn.Linear(hidden_dim, dim, bias=False)
-        self.proj_in = nn.Linear(dim, hidden_dim, bias=False)
-
-    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
-        x = self.proj_out(F.silu(self.proj_in_gate(x)) * self.proj_in(x))
-        return x