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e94400c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | # 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.
import torch
import torch.nn as nn
def swish(x):
return x * torch.sigmoid(x)
class SinusoidalPositionalEncoding(nn.Module):
"""
Produces a sinusoidal encoding of shape (B, T, w)
given timesteps of shape (B, T).
"""
def __init__(self, embedding_dim):
super().__init__()
self.embedding_dim = embedding_dim
def forward(self, timesteps):
# timesteps: shape (B, T)
# We'll compute sin/cos frequencies across dim T
timesteps = timesteps.float() # ensure float
B, T = timesteps.shape
device = timesteps.device
half_dim = self.embedding_dim // 2
# typical log space frequencies for sinusoidal encoding
exponent = -torch.arange(half_dim, dtype=torch.float, device=device) * (
torch.log(torch.tensor(10000.0)) / half_dim
)
# Expand timesteps to (B, T, 1) then multiply
freqs = timesteps.unsqueeze(-1) * exponent.exp() # (B, T, half_dim)
sin = torch.sin(freqs)
cos = torch.cos(freqs)
enc = torch.cat([sin, cos], dim=-1) # (B, T, w)
return enc
class ActionEncoder(nn.Module):
def __init__(self, action_dim, hidden_size):
super().__init__()
self.hidden_size = hidden_size
# W1: R^{w x d}, W2: R^{w x 2w}, W3: R^{w x w}
self.W1 = nn.Linear(action_dim, hidden_size) # (d -> w)
self.W2 = nn.Linear(2 * hidden_size, hidden_size) # (2w -> w)
self.W3 = nn.Linear(hidden_size, hidden_size) # (w -> w)
self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
def forward(self, actions, timesteps):
"""
actions: shape (B, T, action_dim)
timesteps: shape (B,) -- a single scalar per batch item
returns: shape (B, T, hidden_size)
"""
B, T, _ = actions.shape
# 1) Expand each batch's single scalar time 'tau' across all T steps
# so that shape => (B, T)
# e.g. if timesteps is (B,), replicate across T
if timesteps.dim() == 1 and timesteps.shape[0] == B:
# shape (B,) => (B,T)
timesteps = timesteps.unsqueeze(1).expand(-1, T)
else:
raise ValueError(
"Expected `timesteps` to have shape (B,) so we can replicate across T."
)
# 2) Standard action MLP step for shape => (B, T, w)
a_emb = self.W1(actions)
# 3) Get the sinusoidal encoding (B, T, w)
tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)
# 4) Concat along last dim => (B, T, 2w), then W2 => (B, T, w), swish
x = torch.cat([a_emb, tau_emb], dim=-1)
x = swish(self.W2(x))
# 5) Finally W3 => (B, T, w)
x = self.W3(x)
return x
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