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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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, Tuple
import torch
from einops import rearrange
from torch import nn
from torch.distributed import ProcessGroup, get_process_group_ranks
from torchvision import transforms
from cosmos_predict1.diffusion.module.blocks import PatchEmbed
from cosmos_predict1.diffusion.networks.general_dit import GeneralDIT
from cosmos_predict1.utils import log
class DiffusionDecoderGeneralDIT(GeneralDIT):
def __init__(
self,
*args,
is_diffusion_decoder: bool = True,
diffusion_decoder_condition_on_sigma: bool = False,
diffusion_decoder_condition_on_token: bool = False,
diffusion_decoder_token_condition_voc_size: int = 64000,
diffusion_decoder_token_condition_dim: int = 32,
**kwargs,
):
# diffusion decoder setting
self.is_diffusion_decoder = is_diffusion_decoder
self.diffusion_decoder_condition_on_sigma = diffusion_decoder_condition_on_sigma
self.diffusion_decoder_condition_on_token = diffusion_decoder_condition_on_token
self.diffusion_decoder_token_condition_voc_size = diffusion_decoder_token_condition_voc_size
self.diffusion_decoder_token_condition_dim = diffusion_decoder_token_condition_dim
super().__init__(*args, **kwargs)
def initialize_weights(self):
# Initialize transformer layers:
super().initialize_weights()
if self.diffusion_decoder_condition_on_token:
nn.init.constant_(self.token_embedder.weight, 0)
@property
def is_context_parallel_enabled(self):
return self.cp_group is not None
def enable_context_parallel(self, cp_group: ProcessGroup):
cp_ranks = get_process_group_ranks(cp_group)
cp_size = len(cp_ranks)
# Set these attributes for spliting the data after embedding.
self.cp_group = cp_group
# Set these attributes for computing the loss.
self.cp_size = cp_size
# self.pos_embedder.enable_context_parallel(cp_group)
self.pos_embedder.cp_group = cp_group
if self.extra_per_block_abs_pos_emb:
# self.extra_pos_embedder.enable_context_parallel(cp_group)
self.extra_pos_embedder.cp_group = cp_group
# Loop through the model to set up context parallel.
for block in self.blocks.values():
for layer in block.blocks:
if layer.block_type in ["mlp", "ff", "cross_attn", "ca"]:
continue
elif layer.block.attn.backend == "transformer_engine":
layer.block.attn.attn_op.set_context_parallel_group(cp_group, cp_ranks, torch.cuda.Stream())
log.debug(f"[CP] Enable context parallelism with size {cp_size}")
def disable_context_parallel(self):
self.cp_group = None
self.cp_size = None
self.pos_embedder.disable_context_parallel()
if self.extra_per_block_abs_pos_emb:
self.extra_pos_embedder.disable_context_parallel()
# Loop through the model to disable context parallel.
for block in self.blocks.values():
for layer in block.blocks:
if layer.block_type in ["mlp", "ff"]:
continue
elif layer.block_type in ["cross_attn", "ca"]:
continue
else:
layer.block.attn.attn_op.cp_group = None
layer.block.attn.attn_op.cp_ranks = None
log.debug("[CP] Disable context parallelism.")
def build_patch_embed(self):
(
concat_padding_mask,
in_channels,
patch_spatial,
patch_temporal,
model_channels,
is_diffusion_decoder,
diffusion_decoder_token_condition_dim,
diffusion_decoder_condition_on_sigma,
) = (
self.concat_padding_mask,
self.in_channels,
self.patch_spatial,
self.patch_temporal,
self.model_channels,
self.is_diffusion_decoder,
self.diffusion_decoder_token_condition_dim,
self.diffusion_decoder_condition_on_sigma,
)
in_channels = (
in_channels + in_channels
if (is_diffusion_decoder and not self.diffusion_decoder_condition_on_token)
else in_channels
)
in_channels = in_channels + 1 if diffusion_decoder_condition_on_sigma else in_channels
in_channels = (
in_channels + self.diffusion_decoder_token_condition_dim
if self.diffusion_decoder_condition_on_token
else in_channels
)
in_channels = in_channels + 1 if concat_padding_mask else in_channels
self.x_embedder = PatchEmbed(
spatial_patch_size=patch_spatial,
temporal_patch_size=patch_temporal,
in_channels=in_channels,
out_channels=model_channels,
bias=False,
)
if self.diffusion_decoder_condition_on_token:
self.token_embedder = nn.Embedding(
self.diffusion_decoder_token_condition_voc_size, self.diffusion_decoder_token_condition_dim
)
def prepare_embedded_sequence(
self,
x_B_C_T_H_W: torch.Tensor,
fps: Optional[torch.Tensor] = None,
padding_mask: Optional[torch.Tensor] = None,
latent_condition: Optional[torch.Tensor] = None,
latent_condition_sigma: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""
Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks.
Args:
x_B_C_T_H_W (torch.Tensor): video
fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required.
If None, a default value (`self.base_fps`) will be used.
padding_mask (Optional[torch.Tensor]): current it is not used
Returns:
Tuple[torch.Tensor, Optional[torch.Tensor]]:
- A tensor of shape (B, T, H, W, D) with the embedded sequence.
- An optional positional embedding tensor, returned only if the positional embedding class
(`self.pos_emb_cls`) includes 'rope'. Otherwise, None.
Notes:
- If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor.
- The method of applying positional embeddings depends on the value of `self.pos_emb_cls`.
- If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using
the `self.pos_embedder` with the shape [T, H, W].
- If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the `self.pos_embedder`
with the fps tensor.
- Otherwise, the positional embeddings are generated without considering fps.
"""
if self.diffusion_decoder_condition_on_token:
latent_condition = self.token_embedder(latent_condition)
B, _, T, H, W, _ = latent_condition.shape
latent_condition = rearrange(latent_condition, "B 1 T H W D -> (B T) (1 D) H W")
latent_condition = transforms.functional.resize(
latent_condition, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.BILINEAR
)
latent_condition = rearrange(latent_condition, "(B T) D H W -> B D T H W ", B=B, T=T)
x_B_C_T_H_W = torch.cat([x_B_C_T_H_W, latent_condition], dim=1)
if self.diffusion_decoder_condition_on_sigma:
x_B_C_T_H_W = torch.cat([x_B_C_T_H_W, latent_condition_sigma], dim=1)
if self.concat_padding_mask:
padding_mask = transforms.functional.resize(
padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST
)
x_B_C_T_H_W = torch.cat(
[x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1
)
x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)
if self.extra_per_block_abs_pos_emb:
extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps)
else:
extra_pos_emb = None
if "rope" in self.pos_emb_cls.lower():
return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps), extra_pos_emb
if "fps_aware" in self.pos_emb_cls:
x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps) # [B, T, H, W, D]
else:
x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D) # [B, T, H, W, D]
return x_B_T_H_W_D, None, extra_pos_emb
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