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lesjie-wen 2025-02-23 15:21:30 +08:00
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lerobot/common/policies/dexvla/configuration_dexvla.py
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# limitations under the License.
"""Qwen2VL model configuration"""
import os
from typing import Union
from typing import Tuple
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from dataclasses import dataclass, field
from transformers import AutoConfig
from lerobot.common.optim.optimizers import AdamWConfig
from lerobot.common.optim.schedulers import (
CosineDecayWithWarmupSchedulerConfig,
)
from transformers.utils import logging
from transformers import AutoModel, AutoConfig
from lerobot.configs.policies import PreTrainedConfig
from lerobot.common.policies.dexvla.policy_heads.configuration_scaledp import ScaleDPPolicyConfig
from lerobot.common.policies.dexvla.policy_heads.configuration_unet_diffusion import UnetDiffusionPolicyConfig
from lerobot.common.policies.dexvla.qwe2_vla.configuration_qwen2_vla import Qwen2VLAConfig
from lerobot.configs.types import NormalizationMode
logger = logging.get_logger(__name__)
@PreTrainedConfig.register_subclass("dexvla")
@dataclass
class DexVLAConfig(PreTrainedConfig):
# For loading policy head
policy_head_type: str = 'scale_dp_policy'
policy_head_size: str = 'ScaleDP_L'
action_dim: int = 14
state_dim: int = 14
chunk_size: int = 50
n_action_steps: int = 50
n_obs_steps: int = 1
hidden_size: int = 1536
qwen2_vla_path: str = '/media/rl/HDD/data/weights/Qwen2-VL-2B-Instruct'
class Qwen2VLAVisionConfig(PretrainedConfig):
model_type = "dex_vla"
pretrained_path: str = None # pretrained dexvla
using_film: bool = True
llm_loss_weight: float = 1.0
with_llm_head: bool = True
using_reasoning: bool = True
resize_size: tuple = (240, 320)
# Training presets
optimizer_lr: float = 2e-5
optimizer_betas: Tuple[float, float] = (0.9, 0.95)
optimizer_eps: float = 1e-8
optimizer_weight_decay: float = 1e-10
def __init__(
self,
depth=32,
embed_dim=1280,
hidden_size=3584,
hidden_act="quick_gelu",
mlp_ratio=4,
num_heads=16,
in_channels=3,
patch_size=14,
spatial_merge_size=2,
temporal_patch_size=2,
**kwargs,
):
super().__init__(**kwargs)
scheduler_warmup_steps: int = 1_000
scheduler_decay_steps: int = 30_000
scheduler_decay_lr: float = 2.5e-6
self.depth = depth
self.embed_dim = embed_dim
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.mlp_ratio = mlp_ratio
self.num_heads = num_heads
self.in_channels = in_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
normalization_mapping: dict[str, NormalizationMode] = field(
default_factory=lambda: {
# "VISUAL": NormalizationMode.MEAN_STD,
"STATE": NormalizationMode.MEAN_STD,
"ACTION": NormalizationMode.MIN_MAX,
}
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
if config_dict.get("model_type") == "qwen2_vl":
config_dict = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
def __post_init__(self):
if self.n_action_steps > self.chunk_size:
raise ValueError(
f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
)
if self.n_obs_steps != 1:
raise ValueError(
f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
)
if self.using_reasoning:
assert self.using_film, f"using_reasoning requires `using_film=True`"
assert self.with_llm_head, f"using_reasoning requires `with_llm_head=True`"
print("You have set using_reasoning=True, please make sure your data has key 'reasoning'.")
else:
print(f"Warning:DexVLA recommend to use reasoning data which can better handle long-horizon and dexterous tasks. You can set 'using_reaasoning=True'.")
return cls.from_dict(config_dict, **kwargs)
if self.policy_head_type == 'scale_dp_policy':
self.policy_head_config = AutoConfig.for_model(
model_type=self.policy_head_type,
model_size=self.policy_head_size,
cond_dim=self.hidden_size,
action_dim=self.action_dim,
prediction_horizon=self.chunk_size,
state_dim=self.state_dim
)
elif self.policy_head_type == 'unet_diffusion':
self.policy_head_config = AutoConfig.for_model(
model_type=self.policy_head_type,
global_cond_dim=self.hidden_size,
action_dim=self.action_dim,
state_dim=self.state_dim
)
else:
raise ValueError(f'Policy head type {self.policy_head_type} not supported')
self.qwen2_vla_config = AutoConfig.from_pretrained(self.qwen2_vla_path)
def validate_features(self) -> None:
# TODO: implement value error
if not self.image_features and not self.env_state_feature:
raise ValueError("You must provide at least one image or the environment state among the inputs.")
# for i in range(self.empty_cameras):
# key = f"observation.images.empty_camera_{i}"
# empty_camera = PolicyFeature(
# type=FeatureType.VISUAL,
# shape=(3, 480, 640),
# )
# self.input_features[key] = empty_camera
def get_optimizer_preset(self) -> AdamWConfig:
return AdamWConfig(
lr=self.optimizer_lr,
betas=self.optimizer_betas,
eps=self.optimizer_eps,
weight_decay=self.optimizer_weight_decay,
)
def get_scheduler_preset(self):
return CosineDecayWithWarmupSchedulerConfig(
peak_lr=self.optimizer_lr,
decay_lr=self.scheduler_decay_lr,
num_warmup_steps=self.scheduler_warmup_steps,
num_decay_steps=self.scheduler_decay_steps,
)
@property
def observation_delta_indices(self) -> None:
return None
@property
def action_delta_indices(self) -> list:
return list(range(self.chunk_size))
@property
def reward_delta_indices(self) -> None:
return None
class DexVLAConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2VLModel`]. It is used to instantiate a
Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 152064):
Vocabulary size of the Qwen2VL model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen2VLModel`]
hidden_size (`int`, *optional*, defaults to 8192):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 29568):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 80):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 80):
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
vision_config (`Dict`, *optional*):
The config for the visual encoder initialization.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
```python
>>> from transformers import Qwen2VLForConditionalGeneration, Qwen2VLConfig
>>> # Initializing a Qwen2VL style configuration
>>> configuration = Qwen2VLConfig()
>>> # Initializing a model from the Qwen2-VL-7B style configuration
>>> model = Qwen2VLForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_vla"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=152064,
hidden_size=8192,
intermediate_size=29568,
num_hidden_layers=80,
num_attention_heads=64,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-05,
use_cache=True,
tie_word_embeddings=False,
rope_theta=1000000.0,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=80,
attention_dropout=0.0,
vision_config=None,
rope_scaling=None,
# For loading policy head
policy_head_type='dit_diffusion_policy', # dit_diffusion_policy
policy_head_size='DiT_L',
action_dim=10,
state_dim=7,
chunk_size=50,
**kwargs,
):
if isinstance(vision_config, dict):
self.vision_config = Qwen2VLAVisionConfig(**vision_config)
elif vision_config is None:
self.vision_config = Qwen2VLAVisionConfig()
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window
self.max_window_layers = max_window_layers
# for loading policy head
self.policy_head_type = policy_head_type
self.policy_head_config = AutoConfig.for_model(model_type=policy_head_type,
model_size=policy_head_size,
cond_dim=hidden_size, action_dim=action_dim,
prediction_horizon=chunk_size,
state_dim=state_dim)
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.rope_scaling = rope_scaling
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
# and change type from 'mrope' to 'default' because `mrope` does defeault RoPE calculations
# one can set it to "linear"/"dynamic" etc. to have scaled RoPE
# TODO: @raushan update config in the hub
if self.rope_scaling is not None and "type" in self.rope_scaling:
if self.rope_scaling["type"] == "mrope":
self.rope_scaling["type"] = "default"
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self, ignore_keys={"mrope_section"})
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

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lerobot/common/policies/dexvla/modeling_dexvla.py
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