"""
MolmoAct configuration
"""
from typing import Tuple, Optional, Dict, Any
from transformers import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class MolmoActVitConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MolmoActVisionTransformer`].
It is used to instantiate a `MolmoActVisionTransformer` according to the specified arguments,
defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Example:
```python
>>> from transformers import MolmoActVitConfig, MolmoActVisionTransformer
>>> # Initializing a MolmoActVitConfig
>>> configuration = MolmoActVitConfig()
>>> # Initializing a MolmoActVisionTransformer (with random weights)
>>> model = MolmoActVisionTransformer(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "molmoact_vit"
def __init__(
self,
hidden_size: int = 1152,
intermediate_size: int = 4304,
num_hidden_layers: int = 27,
num_attention_heads: int = 16,
num_key_value_heads: int = 16,
head_dim: int = 72,
hidden_act: str = "gelu_pytorch_tanh",
layer_norm_eps: float = 1e-6,
image_default_input_size: Tuple[int, int] = (378, 378),
image_patch_size: int = 14,
image_num_pos: int = 577,
attention_dropout: float = 0.0,
residual_dropout: float = 0.0,
initializer_range: float = 0.02,
float32_attention: bool = True,
use_cls_token: bool = False, # True for OpenCLIP
patch_bias: bool = True, # False for OpenCLIP
pre_layernorm: bool = False, # True for OpenCLIP
**kwargs,
):
super().__init__(**kwargs)
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.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.hidden_act = hidden_act
self.layer_norm_eps = layer_norm_eps
self.image_default_input_size = image_default_input_size
self.image_patch_size = image_patch_size
self.image_num_pos = image_num_pos
self.attention_dropout = attention_dropout
self.residual_dropout = residual_dropout
self.initializer_range = initializer_range
self.float32_attention = float32_attention
self.use_cls_token = use_cls_token
self.patch_bias = patch_bias
self.pre_layernorm = pre_layernorm
@property
def image_num_patch(self):
h, w = self.image_default_input_size
return h // self.image_patch_size, w // self.image_patch_size
class MolmoActAdapterConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of MolmoActAdapter. With MolmoActVitConfig,
It is used to instantiate an MolmoActVisionBackbone according to the specified arguments,
defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Example:
```python
>>> from transformers import MolmoActVitConfig, MolmoActAdapterConfig, MolmoActVisionBackbone
>>> # Initializing a MolmoActVitConfig and a MolmoActAdapterConfig
>>> vit_config = MolmoActVitConfig()
>>> adapter_config = MolmoPoolingConfig()
>>> # Initializing a MolmoActVisionBackbone (with random weights)
>>> model = MolmoActVisionBackbone(vit_config, adapter_config)
>>> # Accessing the model configuration
>>> vit_configuration = model.vit_config
>>> adapter_configuration = model.adapter_config
```"""
def __init__(
self,
vit_layers: Tuple = (-3, -9),
hidden_size: int = 1152,
num_attention_heads: int = 16,
num_key_value_heads: int = 16,
head_dim: int = 72,
float32_attention: bool = True,
attention_dropout: float = 0.0,
residual_dropout: float = 0.0,
hidden_act: str = "silu",
intermediate_size: int = 18944,
text_hidden_size: int = 3584,
image_feature_dropout: float = 0.0,
initializer_range: float = 0.02,
# pooling_mode: str = "indices", # "indices" (SigLIP) or "2x2_attention" (OpenCLIP)
image_padding_embed: Optional[str] = None, # e.g. "pad_and_partial_pad"
**kwargs,
):
super().__init__(**kwargs)
self.vit_layers = vit_layers
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.float32_attention = float32_attention
self.attention_dropout = attention_dropout
self.residual_dropout = residual_dropout
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.text_hidden_size = text_hidden_size
self.image_feature_dropout = image_feature_dropout
self.initializer_range = initializer_range
# self.pooling_mode = pooling_mode
self.image_padding_embed = image_padding_embed
class MolmoActLlmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MolmoActLlm`]. It is used to instantiate a
`MolmoActLlm` according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Example:
```python
>>> from transformers import MolmoActLlmConfig, MolmoActLlm
>>> # Initializing a MolmoActLlmConfig
>>> configuration = MolmoActLlmConfig()
>>> # Initializing a MolmoActLlm (with random weights)
>>> model = MolmoActLlm(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "molmoact_llm"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"blocks.*.self_attn.att_proj": "colwise",
"blocks.*.self_attn.attn_out": "rowwise",
"blocks.*.mlp.ff_proj": "colwise",
"blocks.*.mlp.ff_out": "rowwise",
}
base_model_pp_plan = {
"wte": (["input_ids"], ["inputs_embeds"]),
"blocks": (["hidden_states", "attention_mask"], ["hidden_states"]),
"ln_f": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
hidden_size: int = 3584,
num_attention_heads: int = 28,
num_key_value_heads: Optional[int] = 4,
head_dim: int = 128,
vocab_size: int = 152064,
additional_vocab_size: int = 128,
qkv_bias: bool = True,
num_hidden_layers: int = 48,
intermediate_size: int = 18944,
hidden_act: str = "silu",
embedding_dropout: float=0.0,
attention_dropout: float=0.0,
residual_dropout: float = 0.0,
max_position_embeddings: int = 4096,
rope_theta: float = 1000000.0,
rope_scaling: Dict[str, Any] = None,
use_qk_norm: bool = False,
qk_norm_type: str = "olmo",
layer_norm_eps: int = 1e-6,
norm_after: bool = False,
initializer_range: float = 0.02,
use_cache=True,
tie_word_embeddings=False,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs
)
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.vocab_size = vocab_size
self.additional_vocab_size = additional_vocab_size
self.qkv_bias = qkv_bias
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.embedding_dropout = embedding_dropout
self.attention_dropout = attention_dropout
self.residual_dropout = residual_dropout
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.use_qk_norm = use_qk_norm
self.qk_norm_type = qk_norm_type
self.layer_norm_eps = layer_norm_eps
self.norm_after = norm_after
self.initializer_range = initializer_range
self.use_cache = use_cache
# Validate the correctness of rotary position embeddings parameters
rope_config_validation(self)
class MolmoActConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MolmoActForActionReasoning`].
It is used to instantiate an MolmoAct model according to the specified arguments, defining the model architecture.
Example:
```python
>>> from transformers import MolmoActConfig, MolmoActVitConfig, MolmoActAdapterConfig, MolmoActLlmConfig
>>> # Initializing a MolmoActVitConfig
>>> vit_config = MolmoActVitConfig()
>>> # Initializing a MolmoActAdapterConfig
>>> adapter_config = MolmoActAdapterConfig()
>>> # Initializing a MolmoActLlmConfig
>>> llm_config = MolmoActLlmConfig()
>>> # Initializing a MolmoActConfig
>>> configuration = MolmoActConfig(vit_config, adapter_config, llm_config, image_patch_id=152069)
>>> # Initializing a model
>>> model = MolmoActForActionReasoning(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "molmoact"
sub_configs = {
"llm_config": MolmoActLlmConfig,
"vit_config": MolmoActVitConfig,
"adapter_config": MolmoActAdapterConfig,
}
def __init__(
self,
vit_config: MolmoActVitConfig = None,
adapter_config: MolmoActAdapterConfig = None,
llm_config: MolmoActLlmConfig = None,
image_patch_id: int = None,
initializer_range: float = 0.02,
n_action_bins: int = 256,
norm_stats: dict = {},
**kwargs,
):
super().__init__(**kwargs)
if vit_config is None:
self.vit_config = MolmoActVitConfig()
elif isinstance(vit_config, dict):
self.vit_config = MolmoActVitConfig(**vit_config)
else:
self.vit_config = vit_config
if adapter_config is None:
self.adapter_config = MolmoActAdapterConfig()
elif isinstance(adapter_config, dict):
self.adapter_config = MolmoActAdapterConfig(**adapter_config)
else:
self.adapter_config = adapter_config
if llm_config is None:
self.llm_config = MolmoActLlmConfig()
elif isinstance(llm_config, dict):
self.llm_config = MolmoActLlmConfig(**llm_config)
else:
self.llm_config = llm_config
self.image_patch_id = image_patch_id
self.initializer_range = initializer_range
self.n_action_bins = n_action_bins
self.norm_stats = norm_stats
@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.llm_config.num_attention_heads
@property
def num_key_value_heads(self):
return self.llm_config.num_key_value_heads
@property
def head_dim(self):
return self.llm_config.head_dim
@property
def num_hidden_layers(self):
return self.llm_config.num_hidden_layers
@property
def hidden_size(self):
return self.llm_config.hidden_size
@property
def vocab_size(self):
return self.llm_config.vocab_size
@property
def max_position_embeddings(self):
return self.llm_config.max_position_embeddings
MolmoActVitConfig.register_for_auto_class()
MolmoActAdapterConfig.register_for_auto_class()
MolmoActLlmConfig.register_for_auto_class()
MolmoActConfig.register_for_auto_class()