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| # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. | ||
| # | ||
| # 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. |
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| # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. | ||
| # | ||
| # 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. |
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nemo/collections/diffusion/models/dit/dit_embeddings.py
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| # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. | ||
| # | ||
| # 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. | ||
|
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| import math | ||
| from typing import Dict, Literal, Optional | ||
|
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| import numpy as np | ||
| import torch | ||
| import torch.nn.functional as F | ||
| from diffusers.models.embeddings import TimestepEmbedding, get_3d_sincos_pos_embed | ||
| from einops import rearrange | ||
| from einops.layers.torch import Rearrange | ||
| from megatron.core import parallel_state | ||
| from megatron.core.models.common.embeddings.rotary_pos_embedding import get_pos_emb_on_this_cp_rank | ||
| from megatron.core.transformer.module import MegatronModule | ||
| from torch import nn | ||
|
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|
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| class ParallelTimestepEmbedding(TimestepEmbedding): | ||
| """ | ||
| ParallelTimestepEmbedding is a subclass of TimestepEmbedding that initializes | ||
| the embedding layers with an optional random seed for syncronization. | ||
| Args: | ||
| in_channels (int): Number of input channels. | ||
| time_embed_dim (int): Dimension of the time embedding. | ||
| seed (int, optional): Random seed for initializing the embedding layers. | ||
| If None, no specific seed is set. | ||
| Attributes: | ||
| linear_1 (nn.Module): First linear layer for the embedding. | ||
| linear_2 (nn.Module): Second linear layer for the embedding. | ||
| Methods: | ||
| __init__(in_channels, time_embed_dim, seed=None): Initializes the embedding layers. | ||
| """ | ||
|
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| def __init__(self, in_channels: int, time_embed_dim: int, seed=None): | ||
| super().__init__(in_channels=in_channels, time_embed_dim=time_embed_dim) | ||
| if seed is not None: | ||
| with torch.random.fork_rng(): | ||
| torch.manual_seed(seed) | ||
| self.linear_1.reset_parameters() | ||
| self.linear_2.reset_parameters() | ||
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| def forward(self, x: torch.Tensor) -> torch.Tensor: | ||
| """ | ||
| Computes the positional embeddings for the input tensor. | ||
| Args: | ||
| x (torch.Tensor): Input tensor of shape (B, T, H, W, C). | ||
| Returns: | ||
| torch.Tensor: Positional embeddings of shape (B, T, H, W, C). | ||
| """ | ||
| return super().forward(x.to(torch.bfloat16, non_blocking=True)) | ||
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| def get_pos_emb_on_this_cp_rank(pos_emb, seq_dim): | ||
| """ | ||
| Adjusts the positional embeddings tensor to the current context parallel rank. | ||
| Args: | ||
| pos_emb (torch.Tensor): The positional embeddings tensor. | ||
| seq_dim (int): The sequence dimension index in the positional embeddings tensor. | ||
| Returns: | ||
| torch.Tensor: The adjusted positional embeddings tensor for the current context parallel rank. | ||
| """ | ||
| cp_size = parallel_state.get_context_parallel_world_size() | ||
| cp_rank = parallel_state.get_context_parallel_rank() | ||
| cp_idx = torch.tensor([cp_rank], device="cpu", pin_memory=True).cuda(non_blocking=True) | ||
| pos_emb = pos_emb.view(*pos_emb.shape[:seq_dim], cp_size, -1, *pos_emb.shape[(seq_dim + 1) :]) | ||
| pos_emb = pos_emb.index_select(seq_dim, cp_idx) | ||
| pos_emb = pos_emb.view(*pos_emb.shape[:seq_dim], -1, *pos_emb.shape[(seq_dim + 2) :]) | ||
| return pos_emb | ||
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| class SinCosPosEmb3D(nn.Module): | ||
| """ | ||
| SinCosPosEmb3D is a 3D sine-cosine positional embedding module. | ||
| Args: | ||
| model_channels (int): Number of channels in the model. | ||
| h (int): Length of the height dimension. | ||
| w (int): Length of the width dimension. | ||
| t (int): Length of the temporal dimension. | ||
| spatial_interpolation_scale (float, optional): Scale factor for spatial interpolation. Default is 1.0. | ||
| temporal_interpolation_scale (float, optional): Scale factor for temporal interpolation. Default is 1.0. | ||
| Methods: | ||
| forward(x: torch.Tensor) -> torch.Tensor: | ||
| Computes the positional embeddings for the input tensor. | ||
| Args: | ||
| x (torch.Tensor): Input tensor of shape (B, T, H, W, C). | ||
| Returns: | ||
| torch.Tensor: Positional embeddings of shape (1, T, H, W, C). | ||
| """ | ||
|
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||
| def __init__( | ||
| self, | ||
| *, | ||
| model_channels: int, | ||
| h: int, | ||
| w: int, | ||
| t: int, | ||
| spatial_interpolation_scale=1.0, | ||
| temporal_interpolation_scale=1.0, | ||
| ): | ||
| super().__init__() | ||
| param = get_3d_sincos_pos_embed( | ||
| model_channels, [h, w], t, spatial_interpolation_scale, temporal_interpolation_scale | ||
| ) | ||
| param = rearrange(param, "(b t) (h w) c -> b c t h w", h=h, w=w, b=1) | ||
| self.register_buffer("pos_embedding", torch.from_numpy(param).float(), persistent=False) | ||
|
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| def forward(self, x: torch.Tensor) -> torch.Tensor: | ||
| B, C, T, H, W = x.shape | ||
| cp_size = parallel_state.get_context_parallel_world_size() | ||
| embeddings = self.pos_embedding[..., : T * cp_size, :H, :W] | ||
| embeddings = get_pos_emb_on_this_cp_rank(embeddings, seq_dim=2) | ||
| return embeddings | ||
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| class FactorizedLearnable3DEmbedding(MegatronModule): | ||
| def __init__( | ||
| self, | ||
| config, | ||
| t: int, | ||
| h: int, | ||
| w: int, | ||
| **kwargs, | ||
| ): | ||
| super().__init__(config=config) | ||
| self.emb_t = torch.nn.Embedding(t, config.hidden_size) | ||
| self.emb_h = torch.nn.Embedding(h, config.hidden_size) | ||
| self.emb_w = torch.nn.Embedding(w, config.hidden_size) | ||
|
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| if config.perform_initialization: | ||
| config.init_method(self.emb_t.weight) | ||
| config.init_method(self.emb_h.weight) | ||
| config.init_method(self.emb_w.weight) | ||
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| def forward(self, pos_ids: torch.Tensor): | ||
| return self.emb_t(pos_ids[..., 0]) + self.emb_h(pos_ids[..., 1]) + self.emb_w(pos_ids[..., 2]) |
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