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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.
import torch
from transformers.feature_extraction_utils import BatchFeature
def action_head_pytorch_forward(self, backbone_output, action_input):
backbone_output = self.process_backbone_output(backbone_output)
# Get vision and language embeddings.
vl_embs = backbone_output.backbone_features
embodiment_id = action_input.embodiment_id
# Embed state.
state_features = self.state_encoder(action_input.state, embodiment_id)
# Set initial actions as the sampled noise.
batch_size = vl_embs.shape[0]
device = vl_embs.device
actions = torch.randn(
size=(batch_size, self.config.action_horizon, self.config.action_dim),
dtype=vl_embs.dtype,
device=device,
)
# This attribute is used to ensure the same actions is used for both PyTorch and TensorRT inference
if hasattr(self, "init_actions"):
actions = self.init_actions.expand((batch_size, -1, -1))
num_steps = self.num_inference_timesteps
dt = 1.0 / num_steps
# Run denoising steps.
for t in range(num_steps):
t_cont = t / float(num_steps) # e.g. goes 0, 1/N, 2/N, ...
t_discretized = int(t_cont * self.num_timestep_buckets)
# Embed noised action trajectory.
timesteps_tensor = torch.full(size=(batch_size,), fill_value=t_discretized, device=device)
action_features = self.action_encoder(actions, timesteps_tensor, embodiment_id)
# Maybe add position embedding.
if self.config.add_pos_embed:
pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
action_features = action_features + pos_embs
# Join vision, language, state and action embedding along sequence dimension.
future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
# Run model forward.
model_output = self.model(
hidden_states=sa_embs,
encoder_hidden_states=vl_embs,
timestep=timesteps_tensor,
)
pred = self.action_decoder(model_output, embodiment_id)
pred_velocity = pred[:, -self.action_horizon :]
# Update actions using euler integration.
actions = actions + dt * pred_velocity
return BatchFeature(data={"action_pred": actions})
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