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be46bdea8f
* feat(policies): add Nvidia Gr00t N1.5 model Co-authored-by: lbenhorin <lbenhorin@nvidia.com> Co-authored-by: Aravindh <aravindhs@nvidia.com> Co-authored-by: nv-sachdevkartik <ksachdev@nvidia.com> Co-authored-by: youliangt <youliangt@nvidia.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Jade Choghari <chogharijade@gmail.com> * fix(docs): add groot to index Co-authored-by: sachdevkartik <sachdev.kartik25@gmail.com> --------- Co-authored-by: lbenhorin <lbenhorin@nvidia.com> Co-authored-by: Aravindh <aravindhs@nvidia.com> Co-authored-by: nv-sachdevkartik <ksachdev@nvidia.com> Co-authored-by: youliangt <youliangt@nvidia.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: sachdevkartik <sachdev.kartik25@gmail.com>
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.nn as nn
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def swish(x):
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return x * torch.sigmoid(x)
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class SinusoidalPositionalEncoding(nn.Module):
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"""
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Produces a sinusoidal encoding of shape (B, T, w)
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given timesteps of shape (B, T).
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"""
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def __init__(self, embedding_dim):
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super().__init__()
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self.embedding_dim = embedding_dim
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def forward(self, timesteps):
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# timesteps: shape (B, T)
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# We'll compute sin/cos frequencies across dim T
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timesteps = timesteps.float() # ensure float
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b, t = timesteps.shape
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device = timesteps.device
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half_dim = self.embedding_dim // 2
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# typical log space frequencies for sinusoidal encoding
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exponent = -torch.arange(half_dim, dtype=torch.float, device=device) * (
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torch.log(torch.tensor(10000.0)) / half_dim
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)
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# Expand timesteps to (B, T, 1) then multiply
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freqs = timesteps.unsqueeze(-1) * exponent.exp() # (B, T, half_dim)
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sin = torch.sin(freqs)
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cos = torch.cos(freqs)
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enc = torch.cat([sin, cos], dim=-1) # (B, T, w)
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return enc
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