314 lines
13 KiB
Python
314 lines
13 KiB
Python
#!/usr/bin/env python
|
|
|
|
# Copyright 2025 DexVLA Team and The HuggingFace Inc. team. 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.
|
|
|
|
from collections import deque
|
|
|
|
import torch
|
|
import torchvision.transforms as transforms
|
|
from safetensors.torch import load_file
|
|
from torch import Tensor
|
|
from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
|
|
|
|
from lerobot.common.policies.dexvla.configuration_dexvla import DexVLAConfig
|
|
from lerobot.common.policies.dexvla.robot_data_processor import Qwen2VLAProcess
|
|
from lerobot.common.policies.normalize import Normalize, Unnormalize
|
|
from lerobot.common.policies.pretrained import PreTrainedPolicy
|
|
|
|
|
|
class DexVLAPolicy(PreTrainedPolicy):
|
|
"""Wrapper class around Qwen2VLForConditionalGenerationForVLA model to train and run inference within LeRobot."""
|
|
|
|
config_class = DexVLAConfig
|
|
name = "dexvla"
|
|
|
|
def __init__(
|
|
self,
|
|
config: DexVLAConfig,
|
|
dataset_stats: dict[str, dict[str, Tensor]] | None = None,
|
|
):
|
|
"""
|
|
Args:
|
|
config: Policy configuration class instance or None, in which case the default instantiation of
|
|
the configuration class is used.
|
|
dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected
|
|
that they will be passed with a call to `load_state_dict` before the policy is used.
|
|
"""
|
|
|
|
super().__init__(config)
|
|
config.validate_features()
|
|
self.config = config
|
|
self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats)
|
|
self.normalize_targets = Normalize(
|
|
config.output_features, config.normalization_mapping, dataset_stats
|
|
)
|
|
self.unnormalize_outputs = Unnormalize(
|
|
config.output_features, config.normalization_mapping, dataset_stats
|
|
)
|
|
|
|
for k in ["using_film", "llm_loss_weight", "with_llm_head", "policy_head_config"]:
|
|
setattr(config.qwen2_vla_config, k, config.__dict__[k])
|
|
|
|
# if self.config.training_stage == 2:
|
|
# self.model = Qwen2VLForConditionalGenerationForVLA(config.qwen2_vla_config).to(torch.bfloat16)
|
|
model_base = self.config.qwen2_vl_path
|
|
self.model = AutoModelForCausalLM.from_pretrained(
|
|
model_base,
|
|
config=config.qwen2_vla_config,
|
|
trust_remote_code=True,
|
|
_fast_init=False,
|
|
# attn_implementation="flash_attention_2",
|
|
).to(device="cuda", dtype=torch.bfloat16)
|
|
|
|
if self.config.pretrained_scaledp_path is not None:
|
|
print(
|
|
"\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Loading pretrained ScaleDP weights...<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<"
|
|
)
|
|
pretrain_scaledp_weights = load_file(self.config.pretrained_scaledp_path)
|
|
|
|
keys_to_del_dit = []
|
|
pretrain_scaledp_weights = {
|
|
k[7:] if k.startswith("policy.") else k: v for k, v in pretrain_scaledp_weights.items()
|
|
}
|
|
for k in pretrain_scaledp_weights:
|
|
if "noise_pred" not in k: # del weights of vision backbones
|
|
keys_to_del_dit.append(k)
|
|
if "cond_obs_emb" in k:
|
|
keys_to_del_dit.append(k)
|
|
for k in keys_to_del_dit:
|
|
del pretrain_scaledp_weights[k]
|
|
pretrain_scaledp_weights = {
|
|
k[15:] if k.startswith("noise_pred_net.") else k: v
|
|
for k, v in pretrain_scaledp_weights.items()
|
|
}
|
|
|
|
self.model.policy_head.load_state_dict(pretrain_scaledp_weights, strict=False)
|
|
|
|
self.model.requires_grad_(False)
|
|
self.model.policy_head.requires_grad_(True)
|
|
self.qwen2_vl_processor = AutoProcessor.from_pretrained(config.qwen2_vl_path)
|
|
self.tokenizer = AutoTokenizer.from_pretrained(config.qwen2_vl_path)
|
|
self.vla_processor = Qwen2VLAProcess(
|
|
tokenizer=self.tokenizer, multimodal_processor=self.qwen2_vl_processor
|
|
) # process the input data into VLM format
|
|
|
|
self.resize_size = self.config.resize_size
|
|
ratio = 0.95
|
|
self.transformations = [
|
|
transforms.Resize(size=self.resize_size, antialias=True),
|
|
transforms.RandomCrop(size=[int(self.resize_size[0] * ratio), int(self.resize_size[1] * ratio)]),
|
|
transforms.Resize(self.resize_size, antialias=True),
|
|
transforms.RandomRotation(degrees=[-5.0, 5.0], expand=False),
|
|
transforms.ColorJitter(brightness=0.3, contrast=0.4, saturation=0.5), # , hue=0.08)
|
|
]
|
|
|
|
self.reset()
|
|
|
|
def process_batch(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
|
|
"""Applying DexVLA preprocessing to original data. Including resizing images. Scaling the range of actions, states."""
|
|
batch = self.normalize_inputs(batch)
|
|
batch = self.normalize_targets(batch)
|
|
present_img_keys = [key for key in self.config.image_features if key in batch]
|
|
task_descs = batch["task"]
|
|
try:
|
|
reasonings = batch["reasoning"]
|
|
except KeyError:
|
|
reasonings = ["None."] * len(task_descs)
|
|
|
|
pass
|
|
is_pad = batch["action_is_pad"]
|
|
all_cam_images = []
|
|
for k in present_img_keys:
|
|
all_cam_images.append(batch[k])
|
|
|
|
# construct observations, and scale 0-1 to 0-255
|
|
image_data = torch.stack(all_cam_images) * 255
|
|
image_data = image_data.to(dtype=torch.uint8)
|
|
# construct observations
|
|
qpos_data = batch["observation.state"].float()
|
|
action_data = batch["action"].float()
|
|
|
|
orig_shape = image_data.shape
|
|
image_data = image_data.view(-1, *orig_shape[2:])
|
|
|
|
for transform in self.transformations:
|
|
image_data = transform(image_data)
|
|
|
|
image_data = image_data.view(*orig_shape[:3], *self.resize_size)
|
|
|
|
vl_data = {"images": image_data, "raw_langs": task_descs, "reasonings": reasonings}
|
|
# processing vl_data into qwen2_vl format
|
|
vla_inputs = self.vla_processor.forward(vl_data, use_reasoning=self.config.using_reasoning)
|
|
vla_inputs["states"] = qpos_data
|
|
vla_inputs["is_pad"] = is_pad
|
|
vla_inputs["actions"] = action_data
|
|
return vla_inputs
|
|
|
|
def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Tensor]]:
|
|
processed_batch = self.process_batch(batch)
|
|
|
|
ret = self.model.forward(**processed_batch)
|
|
loss_dict = ret["loss"]
|
|
loss = loss_dict["loss"].mean()
|
|
return loss, loss_dict
|
|
|
|
def dexvla_predict_action(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
actions=None,
|
|
states=None,
|
|
is_pad=None,
|
|
tokenizer=None,
|
|
is_eval=True,
|
|
pixel_values=None,
|
|
attention_mask=None,
|
|
image_grid_spatiotemporal=None,
|
|
):
|
|
input_ids = input_ids.to("cuda")
|
|
with torch.inference_mode():
|
|
outputs = self.model.generate(
|
|
input_ids,
|
|
pixel_values=pixel_values,
|
|
attention_mask=attention_mask,
|
|
image_grid_spatiotemporal=image_grid_spatiotemporal,
|
|
is_eval=is_eval,
|
|
num_beams=1,
|
|
do_sample=False,
|
|
temperature=0.2,
|
|
max_new_tokens=256,
|
|
eos_token_id=tokenizer.eos_token_id, # End of sequence token
|
|
pad_token_id=tokenizer.eos_token_id, # Pad token
|
|
use_cache=True,
|
|
output_hidden_states=True,
|
|
return_dict_in_generate=True,
|
|
)
|
|
|
|
output_ids = outputs.sequences
|
|
# last_hidden_states = outputs.hidden_states[-2][-1]
|
|
input_token_len = input_ids.shape[1]
|
|
n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
|
|
if n_diff_input_output > 0:
|
|
print(f"[Warning] {n_diff_input_output} output_ids are not the same as the input_ids")
|
|
outputs_text = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=False)[0]
|
|
|
|
outputs_text = outputs_text.strip()
|
|
last_hidden_states = [each[-1] for each in outputs.hidden_states] # all hidden states
|
|
all_hidden_states = torch.cat(last_hidden_states, dim=1)
|
|
|
|
action_hidden_states = None
|
|
labels_input = torch.ones((1, input_token_len)) * -100
|
|
labels_output = torch.ones((1, output_ids.shape[1] - input_token_len))
|
|
labels = torch.cat([labels_input, labels_output], dim=1)
|
|
|
|
if self.model.using_film:
|
|
action_hidden_states = self.model.film_forward(
|
|
labels=labels,
|
|
input_ids=output_ids,
|
|
hidden_states=torch.cat(last_hidden_states, dim=1),
|
|
)
|
|
|
|
action = self.model.policy_head(
|
|
actions, action_hidden_states, states.to(all_hidden_states.dtype), is_pad
|
|
)
|
|
return action, outputs_text
|
|
|
|
def tinyvla_predict_action(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
actions=None,
|
|
states=None,
|
|
is_pad=None,
|
|
is_eval=True,
|
|
pixel_values=None,
|
|
attention_mask=None,
|
|
image_grid_spatiotemporal=None,
|
|
):
|
|
input_ids = input_ids.to("cuda")
|
|
with torch.inference_mode():
|
|
all_hidden_states = self.model.forward(
|
|
input_ids,
|
|
pixel_values=pixel_values,
|
|
attention_mask=attention_mask,
|
|
image_grid_spatiotemporal=image_grid_spatiotemporal,
|
|
is_eval=is_eval,
|
|
tinyvla=True,
|
|
)
|
|
|
|
all_hidden_states = torch.mean(all_hidden_states, dim=1).unsqueeze(1)
|
|
|
|
action = self.model.policy_head(
|
|
actions, all_hidden_states, states.to(all_hidden_states.dtype), is_pad
|
|
)
|
|
return action, "tinyvla generates no reasoning"
|
|
|
|
def reset(self):
|
|
"""This should be called whenever the environment is reset."""
|
|
self._action_queue = deque([], maxlen=self.config.n_action_steps)
|
|
|
|
def get_optim_params(self) -> dict:
|
|
return self.parameters()
|
|
|
|
@torch.no_grad
|
|
def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
|
|
"""Select a single action given environment observations.
|
|
|
|
This method wraps `select_actions` in order to return one action at a time for execution in the
|
|
environment. It works by managing the actions in a queue and only calling `select_actions` when the
|
|
queue is empty.
|
|
"""
|
|
self.eval()
|
|
batch = self.normalize_inputs(batch)
|
|
|
|
if len(self._action_queue) == 0:
|
|
present_img_keys = [key for key in self.config.image_features if key in batch]
|
|
try:
|
|
task_descs = batch["task"]
|
|
except KeyError:
|
|
task_descs = " "
|
|
print("No task descriptions found for this task")
|
|
|
|
all_cam_images = []
|
|
for k in present_img_keys:
|
|
all_cam_images.append(batch[k])
|
|
|
|
# construct observations, and scale 0-1 to 0-255
|
|
image_data = torch.stack(all_cam_images) * 255
|
|
image_data = image_data.to(dtype=torch.uint8)
|
|
# construct observations
|
|
qpos_data = batch["observation.state"].float()
|
|
|
|
image_data = image_data.squeeze(0)
|
|
|
|
for transform in self.transformations:
|
|
image_data = transform(image_data)
|
|
|
|
# processing vl_data into qwen2_vl format
|
|
vla_inputs = self.vla_processor.single_forward_process(
|
|
images=image_data, raw_lang=task_descs, reasoning=None, eval=True
|
|
)
|
|
vla_inputs["states"] = qpos_data
|
|
|
|
if self.config.using_film and self.config.with_llm_head: # dexvla
|
|
all_actions, outputs = self.dexvla_predict_action(
|
|
**vla_inputs, is_eval=True, tokenizer=self.tokenizer
|
|
)
|
|
else: # tinyvla
|
|
all_actions, outputs = self.tinyvla_predict_action(**vla_inputs, is_eval=True)
|
|
|
|
actions = self.unnormalize_outputs({"action": all_actions})["action"]
|
|
self._action_queue.extend(actions.transpose(0, 1))
|
|
|
|
return self._action_queue.popleft()
|