mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-23 17:56:07 +00:00
Haoquan Fang
24017e960c
* add molmoact2 policy * add apache headers to molmoact2 files * simplify molmoact2 package imports * align molmoact2 feature validation with eo pattern * remove molmoact2 processor override from factory * guard molmoact2 transformers imports * guard molmoact2 processor transformers import * add scipy dependency to molmoact2 extra * use a single molmoact2 action queue * move molmoact2 config logic into config * fix molmoact2 hf image key resolution * load molmoact2 without remote code * lazy import molmoact2 scipy * format molmoact2 files * skip molmoact2 tests without optional deps * fix molmoact2 pre-commit checks * validate molmoact2 gripper range
554 lines
21 KiB
Python
554 lines
21 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
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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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# ruff: noqa
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"""
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MolmoAct2 configuration
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"""
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from typing import Optional, Any
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from transformers import PretrainedConfig
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from transformers.modeling_rope_utils import rope_config_validation
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class MolmoAct2VitConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MolmoAct2VisionTransformer`].
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It is used to instantiate a `MolmoAct2VisionTransformer` according to the specified arguments,
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defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Example:
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```python
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>>> from transformers import MolmoAct2VitConfig, MolmoAct2VisionTransformer
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>>> # Initializing a MolmoAct2VitConfig
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>>> configuration = MolmoAct2VitConfig()
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>>> # Initializing a MolmoAct2VisionTransformer (with random weights)
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>>> model = MolmoAct2VisionTransformer(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "molmoact2"
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base_config_key = "vit_config"
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def __init__(
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self,
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hidden_size: int = 1152,
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intermediate_size: int = 4304,
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num_hidden_layers: int = 27,
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num_attention_heads: int = 16,
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num_key_value_heads: int = 16,
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head_dim: int = 72,
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hidden_act: str = "gelu_pytorch_tanh",
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layer_norm_eps: float = 1e-6,
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image_default_input_size: tuple[int, int] = (378, 378),
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image_patch_size: int = 14,
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image_num_pos: int = 577,
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attention_dropout: float = 0.0,
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residual_dropout: float = 0.0,
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initializer_range: float = 0.02,
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float32_attention: bool = True,
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attn_implementation: str = "eager",
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**kwargs,
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):
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self.attn_implementation = attn_implementation
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super().__init__(attn_implementation=attn_implementation, **kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.head_dim = head_dim
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self.hidden_act = hidden_act
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self.layer_norm_eps = layer_norm_eps
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self.image_default_input_size = image_default_input_size
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self.image_patch_size = image_patch_size
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self.image_num_pos = image_num_pos
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self.attention_dropout = attention_dropout
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self.residual_dropout = residual_dropout
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self.initializer_range = initializer_range
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self.float32_attention = float32_attention
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@property
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def image_num_patch(self):
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h, w = self.image_default_input_size
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return h // self.image_patch_size, w // self.image_patch_size
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class MolmoAct2AdapterConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of MolmoAct2Adapter. With MolmoAct2VitConfig,
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It is used to instantiate an MolmoAct2VisionBackbone according to the specified arguments,
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defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Example:
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```python
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>>> from transformers import MolmoAct2VitConfig, MolmoAct2AdapterConfig, MolmoAct2VisionBackbone
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>>> # Initializing a MolmoAct2VitConfig and a MolmoAct2AdapterConfig
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>>> vit_config = MolmoAct2VitConfig()
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>>> adapter_config = MolmoPoolingConfig()
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>>> # Initializing a MolmoAct2VisionBackbone (with random weights)
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>>> model = MolmoAct2VisionBackbone(vit_config, adapter_config)
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>>> # Accessing the model configuration
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>>> vit_configuration = model.vit_config
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>>> adapter_configuration = model.adapter_config
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```"""
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model_type = "molmoact2"
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base_config_key = "adapter_config"
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def __init__(
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self,
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vit_layers: tuple = (-3, -9),
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pooling_attention_mask: bool = False,
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hidden_size: int = 1152,
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num_attention_heads: int = 16,
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num_key_value_heads: int = 16,
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head_dim: int = 72,
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float32_attention: bool = True,
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attention_dropout: float = 0.0,
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residual_dropout: float = 0.0,
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hidden_act: str = "silu",
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intermediate_size: int = 18944,
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text_hidden_size: int = 3584,
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image_feature_dropout: float = 0.0,
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initializer_range: float = 0.02,
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attn_implementation: str = "eager",
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**kwargs,
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):
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self.attn_implementation = attn_implementation
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super().__init__(attn_implementation=attn_implementation, **kwargs)
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self.vit_layers = vit_layers
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self.pooling_attention_mask = pooling_attention_mask
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.head_dim = head_dim
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self.float32_attention = float32_attention
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self.attention_dropout = attention_dropout
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self.residual_dropout = residual_dropout
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.text_hidden_size = text_hidden_size
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self.image_feature_dropout = image_feature_dropout
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self.initializer_range = initializer_range
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class MolmoAct2TextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MolmoAct2TextModel`]. It is used to instantiate a
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`MolmoAct2TextModel` according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Example:
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```python
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>>> from transformers import MolmoAct2TextConfig, MolmoAct2TextModel
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>>> # Initializing a MolmoAct2TextConfig
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>>> configuration = MolmoAct2TextConfig()
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>>> # Initializing a MolmoAct2TextModel (with random weights)
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>>> model = MolmoAct2TextModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "molmoact2_text"
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base_config_key = "text_config"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"blocks.*.self_attn.att_proj": "colwise",
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"blocks.*.self_attn.attn_out": "rowwise",
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"blocks.*.mlp.ff_proj": "colwise",
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"blocks.*.mlp.ff_out": "rowwise",
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}
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base_model_pp_plan = {
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"wte": (["input_ids"], ["inputs_embeds"]),
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"blocks": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"ln_f": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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hidden_size: int = 3584,
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num_attention_heads: int = 28,
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num_key_value_heads: int | None = 4,
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head_dim: int = 128,
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vocab_size: int = 152064,
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additional_vocab_size: int = 128,
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qkv_bias: bool = True,
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num_hidden_layers: int = 48,
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intermediate_size: int = 18944,
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hidden_act: str = "silu",
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embedding_dropout: float = 0.0,
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attention_dropout: float = 0.0,
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residual_dropout: float = 0.0,
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max_position_embeddings: int = 4096,
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rope_theta: float = 1000000.0,
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rope_scaling: dict[str, Any] = None,
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rope_scaling_layers: list[int] | None = None,
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use_qk_norm: bool = False,
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qk_norm_type: str = "olmo",
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layer_norm_eps: int = 1e-6,
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norm_after: bool = False,
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initializer_range: float = 0.02,
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use_cache=True,
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tie_word_embeddings=False,
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attn_implementation: str = "eager",
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**kwargs,
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):
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self.attn_implementation = attn_implementation
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super().__init__(
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tie_word_embeddings=tie_word_embeddings, attn_implementation=attn_implementation, **kwargs
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)
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.head_dim = head_dim
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self.vocab_size = vocab_size
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self.additional_vocab_size = additional_vocab_size
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self.qkv_bias = qkv_bias
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self.num_hidden_layers = num_hidden_layers
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.embedding_dropout = embedding_dropout
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self.attention_dropout = attention_dropout
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self.residual_dropout = residual_dropout
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self.max_position_embeddings = max_position_embeddings
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.rope_scaling_layers = rope_scaling_layers
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self.use_qk_norm = use_qk_norm
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self.qk_norm_type = qk_norm_type
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self.layer_norm_eps = layer_norm_eps
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self.norm_after = norm_after
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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# Validate the correctness of rotary position embeddings parameters
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rope_config_validation(self)
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class MolmoAct2ActionExpertConfig(PretrainedConfig):
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r"""Configuration for the MolmoAct2 modern action expert."""
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model_type = "molmoact2_action_expert"
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base_config_key = "action_expert_config"
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def __init__(
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self,
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max_action_horizon: int = 32,
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max_action_dim: int = 32,
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hidden_size: int = 1024,
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num_layers: int = 32,
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num_heads: int = 16,
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mlp_ratio: float = 8.0 / 3.0,
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ffn_multiple_of: int = 256,
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timestep_embed_dim: int = 256,
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dropout: float = 0.0,
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attn_dropout: float = 0.0,
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context_layer_norm: bool = True,
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qk_norm: bool = True,
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qk_norm_eps: float = 1e-6,
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rope: bool = True,
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causal_attn: bool = False,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.max_action_horizon = max_action_horizon
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self.max_action_dim = max_action_dim
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.num_heads = num_heads
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self.mlp_ratio = mlp_ratio
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self.ffn_multiple_of = ffn_multiple_of
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self.timestep_embed_dim = timestep_embed_dim
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self.dropout = dropout
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self.attn_dropout = attn_dropout
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self.context_layer_norm = context_layer_norm
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self.qk_norm = qk_norm
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self.qk_norm_eps = qk_norm_eps
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self.rope = rope
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self.causal_attn = causal_attn
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def to_dict(self):
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output = super().to_dict()
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# These are derived from the parent MolmoAct2Config for HF exports. Keeping
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# them out of the public nested config avoids duplicated sources of truth.
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output.pop("max_action_horizon", None)
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output.pop("max_action_dim", None)
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return output
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class MolmoAct2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MolmoAct2ForConditionalGeneration`].
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It is used to instantiate an MolmoAct2 model according to the specified arguments, defining the model architecture.
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Example:
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```python
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>>> from transformers import MolmoAct2Config, MolmoAct2VitConfig, MolmoAct2AdapterConfig, MolmoAct2TextConfig
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>>> # Initializing a MolmoAct2VitConfig
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>>> vit_config = MolmoAct2VitConfig()
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>>> # Initializing a MolmoAct2AdapterConfig
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>>> adapter_config = MolmoAct2AdapterConfig()
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>>> # Initializing a MolmoAct2TextConfig
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>>> text_config = MolmoAct2TextConfig()
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>>> # Initializing a MolmoAct2Config
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>>> configuration = MolmoAct2Config(
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>>> vit_config=vit_config,
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>>> adapter_config=adapter_config,
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>>> text_config=text_config,
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>>> image_start_token_id=151936,
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>>> image_end_token_id=151937,
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>>> image_patch_id=151938,
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>>> image_col_id=151939,
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>>> low_res_image_start_token_id=151940,
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>>> image_low_res_id=151942,
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>>> frame_start_token_id=151943,
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>>> frame_end_token_id=151944,
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>>> )
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>>> # Initializing a model
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>>> model = MolmoAct2ForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "molmoact2"
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sub_configs = {
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"text_config": MolmoAct2TextConfig,
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"vit_config": MolmoAct2VitConfig,
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"adapter_config": MolmoAct2AdapterConfig,
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"action_expert_config": MolmoAct2ActionExpertConfig,
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}
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def __init__(
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self,
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vit_config: MolmoAct2VitConfig = None,
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adapter_config: MolmoAct2AdapterConfig = None,
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text_config: MolmoAct2TextConfig = None,
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action_expert_config: MolmoAct2ActionExpertConfig = None,
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image_start_token_id: int = None,
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low_res_image_start_token_id: int = None,
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image_end_token_id: int = None,
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image_low_res_id: int = None,
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image_patch_id: int = None,
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image_col_id: int = None,
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frame_start_token_id: int = None,
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frame_end_token_id: int = None,
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use_frame_special_tokens: bool = True,
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initializer_range: float = 0.02,
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add_action_expert: bool = True,
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max_action_dim: int = 32,
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max_action_horizon: int = 30,
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n_obs_steps: int = 30,
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action_mode: str = "both",
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state_format: str = "discrete",
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flow_matching_num_steps: int = 10,
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flow_matching_cutoff: float = 1.0,
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flow_matching_time_offset: float = 0.001,
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flow_matching_time_scale: float = 0.999,
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flow_matching_beta_alpha: float = 1.0,
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flow_matching_beta_beta: float = 1.5,
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mask_action_dim_padding: bool = True,
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enable_depth_reasoning: bool = False,
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depth_mode: int = 2,
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num_depth_codes: int = 100,
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action_expert_depth_gate: bool = False,
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action_expert_depth_gate_per_layer: bool = False,
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action_expert_depth_gate_init_bias: float = -4.0,
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action_output_token_id: int = None,
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action_start_token_id: int = None,
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action_end_token_id: int = None,
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action_token_start_id: int = None,
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num_action_tokens: int = 0,
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depth_output_token_id: int = None,
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depth_start_token_id: int = None,
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depth_end_token_id: int = None,
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depth_token_start_id: int = None,
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num_depth_tokens: int = 0,
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state_start_token_id: int = None,
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state_end_token_id: int = None,
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state_token_start_id: int = None,
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num_state_tokens: int = 0,
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add_setup_tokens: bool = True,
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add_control_tokens: bool = True,
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norm_stats_filename: str = "norm_stats.json",
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**kwargs,
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):
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super().__init__(**kwargs)
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if vit_config is None:
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self.vit_config = MolmoAct2VitConfig()
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elif isinstance(vit_config, dict):
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self.vit_config = MolmoAct2VitConfig(**vit_config)
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else:
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self.vit_config = vit_config
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if adapter_config is None:
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self.adapter_config = MolmoAct2AdapterConfig()
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elif isinstance(adapter_config, dict):
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self.adapter_config = MolmoAct2AdapterConfig(**adapter_config)
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else:
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self.adapter_config = adapter_config
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if text_config is None:
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self.text_config = MolmoAct2TextConfig()
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elif isinstance(text_config, dict):
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self.text_config = MolmoAct2TextConfig(**text_config)
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else:
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self.text_config = text_config
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self.add_action_expert = bool(add_action_expert)
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if not self.add_action_expert:
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self.action_expert_config = None
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elif action_expert_config is None:
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self.action_expert_config = MolmoAct2ActionExpertConfig(
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max_action_horizon=max_action_horizon,
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max_action_dim=max_action_dim,
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num_layers=self.text_config.num_hidden_layers,
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)
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elif isinstance(action_expert_config, dict):
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self.action_expert_config = MolmoAct2ActionExpertConfig(**action_expert_config)
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else:
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self.action_expert_config = action_expert_config
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if self.add_action_expert:
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self.action_expert_config.max_action_dim = int(max_action_dim)
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self.action_expert_config.max_action_horizon = int(max_action_horizon)
|
|
self._validate_release_action_config(
|
|
state_format=state_format,
|
|
)
|
|
self.image_start_token_id = image_start_token_id
|
|
self.low_res_image_start_token_id = low_res_image_start_token_id
|
|
self.image_end_token_id = image_end_token_id
|
|
self.image_low_res_id = image_low_res_id
|
|
self.image_high_res_id = image_patch_id
|
|
self.image_patch_id = image_patch_id
|
|
self.image_col_id = image_col_id
|
|
self.frame_start_token_id = frame_start_token_id
|
|
self.frame_end_token_id = frame_end_token_id
|
|
self.use_frame_special_tokens = use_frame_special_tokens
|
|
self.initializer_range = initializer_range
|
|
self.max_action_dim = max_action_dim
|
|
self.max_action_horizon = max_action_horizon
|
|
self.n_obs_steps = n_obs_steps
|
|
self.action_mode = action_mode
|
|
self.state_format = state_format
|
|
self.flow_matching_num_steps = flow_matching_num_steps
|
|
self.flow_matching_cutoff = flow_matching_cutoff
|
|
self.flow_matching_time_offset = flow_matching_time_offset
|
|
self.flow_matching_time_scale = flow_matching_time_scale
|
|
self.flow_matching_beta_alpha = flow_matching_beta_alpha
|
|
self.flow_matching_beta_beta = flow_matching_beta_beta
|
|
self.mask_action_dim_padding = mask_action_dim_padding
|
|
self.enable_depth_reasoning = enable_depth_reasoning
|
|
self.depth_mode = depth_mode
|
|
self.num_depth_codes = num_depth_codes
|
|
self.action_expert_depth_gate = action_expert_depth_gate
|
|
self.action_expert_depth_gate_per_layer = action_expert_depth_gate_per_layer
|
|
self.action_expert_depth_gate_init_bias = action_expert_depth_gate_init_bias
|
|
self.action_output_token_id = action_output_token_id
|
|
self.action_start_token_id = action_start_token_id
|
|
self.action_end_token_id = action_end_token_id
|
|
self.action_token_start_id = action_token_start_id
|
|
self.num_action_tokens = num_action_tokens
|
|
self.depth_output_token_id = depth_output_token_id
|
|
self.depth_start_token_id = depth_start_token_id
|
|
self.depth_end_token_id = depth_end_token_id
|
|
self.depth_token_start_id = depth_token_start_id
|
|
self.num_depth_tokens = num_depth_tokens
|
|
self.state_start_token_id = state_start_token_id
|
|
self.state_end_token_id = state_end_token_id
|
|
self.state_token_start_id = state_token_start_id
|
|
self.num_state_tokens = num_state_tokens
|
|
self.add_setup_tokens = add_setup_tokens
|
|
self.add_control_tokens = add_control_tokens
|
|
self.norm_stats_filename = norm_stats_filename
|
|
|
|
@staticmethod
|
|
def _validate_release_action_config(
|
|
*,
|
|
state_format: str,
|
|
) -> None:
|
|
if state_format != "discrete":
|
|
raise ValueError("MolmoAct2 HF export supports only state_format='discrete'.")
|
|
|
|
@property
|
|
def image_num_patch(self):
|
|
assert self.vit_config is not None
|
|
return self.vit_config.image_num_patch
|
|
|
|
@property
|
|
def num_attention_heads(self):
|
|
return self.text_config.num_attention_heads
|
|
|
|
@property
|
|
def num_key_value_heads(self):
|
|
return self.text_config.num_key_value_heads
|
|
|
|
@property
|
|
def head_dim(self):
|
|
return self.text_config.head_dim
|
|
|
|
@property
|
|
def num_hidden_layers(self):
|
|
return self.text_config.num_hidden_layers
|
|
|
|
@property
|
|
def hidden_size(self):
|
|
return self.text_config.hidden_size
|
|
|
|
@property
|
|
def vocab_size(self):
|
|
return self.text_config.vocab_size
|
|
|
|
@property
|
|
def max_position_embeddings(self):
|
|
return self.text_config.max_position_embeddings
|
|
|
|
|
|
MolmoAct2VitConfig.register_for_auto_class()
|
|
MolmoAct2AdapterConfig.register_for_auto_class()
|
|
MolmoAct2TextConfig.register_for_auto_class()
|
|
MolmoAct2ActionExpertConfig.register_for_auto_class()
|
|
MolmoAct2Config.register_for_auto_class()
|