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# SPDX-License-Identifier: Apache-2.0
from typing import Optional
import torch
import torch.nn as nn
from einops import rearrange
class PositionalEncoding(nn.Module):
"""Non-learned positional encoding."""
def __init__(
self,
d_model: int,
dropout: Optional[float] = 0.1,
max_len: Optional[int] = 5000,
):
"""
Args:
d_model (int): input dim
dropout (Optional[float] = 0.1): dropout probability on output
max_len (Optional[int] = 5000): maximum sequence length
"""
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
# Note: have to replace torch.exp() and math.log() with torch.pow()
# due to MKL exp() and ln() throws floating point exceptions on certain CPUs
# see corresponding commit and MR
div_term = torch.pow(10000.0, -torch.arange(0, d_model, 2).float() / d_model)
# div_term = torch.exp(
# torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)
# )
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0) # [1, T, D]
self.register_buffer("pe", pe, persistent=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Apply positional encoding to input sequence.
Args:
x (torch.Tensor): [B, T, D] input motion sequence
Returns:
torch.Tensor: [B, T, D] input motion with PE added to it (and optionally dropout)
"""
x = x + self.pe[:, : x.shape[1], :]
return self.dropout(x)
class EncoderTransformer(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int,
num_frames_per_token: int,
latent_dim: int,
num_heads: int,
ff_size: int,
dropout: float,
activation: str,
norm_first: bool,
num_layers: int,
pe_dropout: float,
is_causal: bool = True,
):
super().__init__()
self.is_causal = is_causal
self.num_frames_per_token = num_frames_per_token
self.latent_dim = latent_dim
self.num_heads = num_heads
self.ff_size = ff_size
self.dropout = dropout
self.activation = activation
self.norm_first = norm_first
self.num_layers = num_layers
self.pe_dropout = pe_dropout
self.input_proj = nn.Linear(input_dim * num_frames_per_token, latent_dim)
self.sequence_pos_encoder = PositionalEncoding(latent_dim, pe_dropout)
trans_enc_layer = nn.TransformerEncoderLayer(
d_model=latent_dim,
nhead=num_heads,
dim_feedforward=ff_size,
dropout=dropout,
activation=activation,
batch_first=True,
norm_first=norm_first,
)
# technically we don't need to disable nested tensors here, just
# need to make sure flash attention is not used. One way is to set
# torch.backends.mha.set_fastpath_enabled(False) but this only
# works on torch>=2.3.0. We should test how much perf is lost without
# using nested tensors.
self.seqTransEncoder = nn.TransformerEncoder(trans_enc_layer, num_layers=num_layers, enable_nested_tensor=False)
self.output_proj = nn.Linear(latent_dim, output_dim)
def forward(self, x, motion_pad_mask: Optional[torch.BoolTensor] = None):
"""Tranformer-based encoder.
Args:
x (torch.Tensor): motions frames [batch_size, numFrames, latent_dim]
motion_pad_mask (Optional[torch.BoolTensor]): shape -> [batch, num_frames] (dtype=bool)
Returns:
torch.Tensor: latent embeddings [batch_size, num_tokens, latent_dim]
"""
# reshape motion frames to [batch_size, num_tokens, num_frames_per_token * latent_dim]
x = rearrange(x, "b (t f) d -> b t (f d)", f=self.num_frames_per_token)
x = self.input_proj(x) # input projection
x = self.sequence_pos_encoder(x) # positional encoding
attention_mask = (
torch.nn.Transformer.generate_square_subsequent_mask(x.shape[1], device=x.device, dtype=torch.bool)
if self.is_causal
else None
) # create causal mask
if motion_pad_mask is not None:
motion_pad_mask = rearrange(motion_pad_mask, "b (t f) -> b t f", f=self.num_frames_per_token)
# `> 0.5` casts to bool so the downstream `~` traces to ONNX `Not`
# on a BOOL tensor (TRT rejects `Not` on FLOAT). Accepts a float mask
# (TRT I/O convention) or a bool mask (eager); semantics are identical.
token_pad_mask = (motion_pad_mask > 0.5).all(dim=-1) # [B, num_tokens]
# negate the mask to get the padding mask, True means not allowed in attention
src_key_padding_mask = ~token_pad_mask
else:
src_key_padding_mask = None
x = self.seqTransEncoder(
x, mask=attention_mask, src_key_padding_mask=src_key_padding_mask
) # transformer encoder layers
output_embeddings = self.output_proj(x) # output latent embedding [batch_size, num_tokens, output_dim]
return output_embeddings
class DoubleCondDecoderTransformer(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int,
num_frames_per_token: int,
latent_dim: int,
num_heads: int,
ff_size: int,
dropout: float,
activation: str,
norm_first: bool,
num_layers: int,
pe_dropout: float,
is_causal: bool = True,
target_cond_dim: int = -1,
external_cond_dim: int = -1,
):
super().__init__()
self.is_causal = is_causal
self.num_frames_per_token = num_frames_per_token
self.latent_dim = latent_dim
self.num_heads = num_heads
self.ff_size = ff_size
self.dropout = dropout
self.activation = activation
self.norm_first = norm_first
self.num_layers = num_layers
self.pe_dropout = pe_dropout
# target and external condition configs
self._target_cond_dim, self._external_cond_dim = (
target_cond_dim,
external_cond_dim,
)
self._HAS_EXTERNAL_COND, self._HAS_TARGET_COND = (
external_cond_dim > 0,
target_cond_dim > 0,
)
self.input_proj = nn.Linear(input_dim, latent_dim)
self.sequence_pos_encoder = PositionalEncoding(latent_dim, pe_dropout)
trans_enc_layer = nn.TransformerEncoderLayer(
d_model=latent_dim,
nhead=num_heads,
dim_feedforward=ff_size,
dropout=dropout,
activation=activation,
batch_first=True,
norm_first=norm_first,
)
# technically we don't need to disable nested tensors here, just
# need to make sure flash attention is not used. One way is to set
# torch.backends.mha.set_fastpath_enabled(False) but this only
# works on torch>=2.3.0. We should test how much perf is lost without
# using nested tensors.
self.seqTransEncoder = nn.TransformerEncoder(trans_enc_layer, num_layers=num_layers, enable_nested_tensor=False)
"""Step 2: external cond embeddings.
At each layer, the external embedding will be merged with the hidden state. The external
condition is dense and expected to be always available in each frame.
"""
if self._HAS_EXTERNAL_COND:
external_cond_blocks = []
cond_feat_dim = num_frames_per_token * self._external_cond_dim
external_cond_blocks.append(nn.Linear(cond_feat_dim + latent_dim, latent_dim))
external_cond_blocks.append(nn.ReLU())
self.external_cond_blocks = nn.Sequential(*external_cond_blocks)
"""Step 3: target cond embeddings.
The target condition is sparse and expected to be available in certain frames. we replace
the hidden state with the target condition embeddings for given frames. In earlier layers
where each position corresponds to multiple frames, we reshape the hidden states to map to
each frame position (see @forward method)
"""
if self._HAS_TARGET_COND:
target_cond_blocks = []
assert latent_dim % num_frames_per_token == 0, (
"latent_dim % num_frames_per_token needs to 0 so that hidden can be split for each frame in earlier layers."
)
target_cond_blocks.append(nn.Linear(self._target_cond_dim, int(latent_dim / num_frames_per_token)))
target_cond_blocks.append(nn.ReLU())
self.target_cond_blocks = nn.Sequential(*target_cond_blocks)
# output projection
self.output_proj = nn.Linear(latent_dim, output_dim * num_frames_per_token)
def forward(
self,
x: torch.Tensor,
external_cond: torch.Tensor = None,
target_cond: Optional[torch.Tensor] = None,
has_target_cond: Optional[torch.Tensor] = None,
motion_pad_mask: Optional[torch.BoolTensor] = None,
):
"""@brief: the decoder could take the external condition and target condition as input.
@params x: shape -> [batch, num_tokens, latent_dim]
@params external_cond: shape -> [batch, num_frames, external_cond_dim]
@params target_cond: shape -> [batch, num_frames, target_cond_dim]
@params has_target_cond: shape -> [batch, num_frames] (dtype=bool)
@params motion_pad_mask: shape -> [batch, num_frames] (dtype=bool)
"""
batch_size = x.shape[0]
num_tokens = x.shape[1]
num_frames_per_token = self.num_frames_per_token
num_frames = num_tokens * num_frames_per_token
h = self.input_proj(x) # [batch, num_tokens, latent_dim]
if (not self._HAS_TARGET_COND) or target_cond is None or has_target_cond is None: # no target condition
pass
else: # replace parts of tokens with target condition
h_target_cond = self.target_cond_blocks(target_cond) # [batch, num_frames, latent_dim]
h = h.reshape([batch_size, num_frames, self.latent_dim // self.num_frames_per_token])
h = torch.where(has_target_cond[:, :, None], h_target_cond, h)
h = h.reshape([batch_size, num_tokens, self.latent_dim])
if self._HAS_EXTERNAL_COND: # concatenate external condition
assert external_cond is not None and external_cond.shape[1] == num_frames
external_cond = external_cond.reshape(
[batch_size, num_tokens, -1]
) # [batch, num_tokens, external_cond_dim * num_frames_per_token]
h = torch.cat(
[h, external_cond], dim=-1
) # [batch, num_tokens, latent_dim + external_cond_dim * num_frames_per_token]
h = self.external_cond_blocks(h) # [batch, num_tokens, latent_dim]
h = self.sequence_pos_encoder(h) # positional encoding
attention_mask = (
torch.nn.Transformer.generate_square_subsequent_mask(h.shape[1], device=h.device, dtype=torch.bool)
if self.is_causal
else None
) # create causal mask
if motion_pad_mask is not None:
motion_pad_mask = rearrange(motion_pad_mask, "b (t f) -> b t f", f=self.num_frames_per_token)
# `> 0.5` casts to bool so the downstream `~` traces to ONNX `Not`
# on a BOOL tensor (TRT rejects `Not` on FLOAT). Accepts a float mask
# (TRT I/O convention) or a bool mask (eager); semantics are identical.
token_pad_mask = (motion_pad_mask > 0.5).all(dim=-1) # [B, num_tokens]
# negate the mask to get the padding mask, True means not allowed in attention
src_key_padding_mask = ~token_pad_mask
else:
src_key_padding_mask = None
h = self.seqTransEncoder(
h, mask=attention_mask, src_key_padding_mask=src_key_padding_mask
) # transformer encoder layers
h = self.output_proj(h) # output projection
h = rearrange(h, "b t (f d) -> b (t f) d", f=self.num_frames_per_token) # [batch, num_frames, output_dim]
return h
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