openvla-7b-oft-finetuned-libero-10 / modeling_prismatic.py
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"""
modeling_prismatic.py
Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions.
Inherits from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained,
but exactly replicate the logic in `prismatic.models.vlms.prismatic.py`.
"""
import logging
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
import numpy as np
import timm
import tokenizers
import torch
import torch.nn as nn
import transformers
from timm.models.vision_transformer import LayerScale
from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import ModelOutput
from prismatic.training.train_utils import (
get_current_action_mask,
get_next_actions_mask,
)
from prismatic.vla.constants import (
ACTION_DIM,
ACTION_PROPRIO_NORMALIZATION_TYPE,
ACTION_TOKEN_BEGIN_IDX,
IGNORE_INDEX,
NUM_ACTIONS_CHUNK,
STOP_INDEX,
NormalizationType,
)
from .configuration_prismatic import OpenVLAConfig, PrismaticConfig
# Set up logger
logger = logging.getLogger(__name__)
# === Utility Functions for Monkey-Patching ===
def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
def wrapper(*args: Any, **kwargs: Any) -> Any:
result = fn(*args, **kwargs)
return result[0] if isinstance(result, tuple) else result
return wrapper
# HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
# =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
# =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
def ls_apply_patch(ls_module: LayerScale):
ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
del ls_module.gamma
# === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
class PrismaticVisionBackbone(nn.Module):
"""
Vision backbone for Prismatic models that handles image feature extraction.
Supports both single backbone (e.g., SigLIP) and fused backbone (e.g., SigLIP + DINOv2) configurations.
For fused backbones, features from both models are concatenated along the feature dimension.
"""
def __init__(
self,
use_fused_vision_backbone: bool,
image_sizes: List[int],
timm_model_ids: List[str],
timm_override_act_layers: List[Optional[str]],
) -> None:
"""
Initialize the vision backbone.
Args:
use_fused_vision_backbone: Whether to use two backbones and fuse their features
image_sizes: List of image sizes for each backbone
timm_model_ids: List of TIMM model IDs to use for each backbone
timm_override_act_layers: List of activation layer overrides for each backbone
"""
super().__init__()
self.use_fused_vision_backbone = use_fused_vision_backbone
self.num_images_in_input = 1 # Default value, can be overridden later
# Validate number of (fused) vision backbones
if len(timm_model_ids) > 2:
raise ValueError("Prismatic models only support up to 2 (fused) vision backbones!")
# Create primary featurizer
self.featurizer = self._create_featurizer(
model_id=timm_model_ids[0], img_size=image_sizes[0], act_layer=timm_override_act_layers[0]
)
self.embed_dim = self.featurizer.embed_dim
# Create secondary featurizer if using fused backbone
if self.use_fused_vision_backbone:
self.fused_featurizer = self._create_featurizer(
model_id=timm_model_ids[1], img_size=image_sizes[1], act_layer=timm_override_act_layers[1]
)
self.embed_dim += self.fused_featurizer.embed_dim
# Patch LayerScale modules for HF compatibility
self._patch_layer_scales()
def _create_featurizer(self, model_id: str, img_size: int, act_layer: Optional[str]) -> nn.Module:
"""
Create a TIMM-based featurizer model with appropriate configurations.
Args:
model_id: The TIMM model ID to load
img_size: Input image size for the model
act_layer: Override for the activation layer type
Returns:
A configured featurizer model
"""
featurizer = timm.create_model(
model_id,
pretrained=False,
num_classes=0,
img_size=img_size,
act_layer=act_layer,
)
# Monkey-patch the forward function to extract the second-to-last layer features
num_blocks = len(featurizer.blocks)
featurizer.forward = unpack_tuple(partial(featurizer.get_intermediate_layers, n={num_blocks - 2}))
return featurizer
def _patch_layer_scales(self) -> None:
"""
Patch all LayerScale modules to be compatible with HF's parameter naming.
HF Transformers overwrites parameters with names containing 'gamma',
so we need to rename and modify the forward method.
"""
# Patch primary featurizer
for module in self.featurizer.modules():
if isinstance(module, LayerScale):
ls_apply_patch(module)
# Patch secondary featurizer if it exists
if self.use_fused_vision_backbone:
for module in self.fused_featurizer.modules():
if isinstance(module, LayerScale):
ls_apply_patch(module)
def get_num_patches(self) -> int:
"""
Returns the number of vision patches output by the vision backbone.
Returns:
Number of patches per image
"""
return self.featurizer.patch_embed.num_patches
def get_num_images_in_input(self) -> int:
"""
Returns the number of input images for the vision backbone.
Returns:
Number of images expected in the input
"""
return self.num_images_in_input
def set_num_images_in_input(self, num_images_in_input: int) -> None:
"""
Sets the number of input images for the vision backbone.
Args:
num_images_in_input: Number of images to expect in the input
"""
self.num_images_in_input = num_images_in_input
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
"""
Implements the forward pass for the vision backbone.
If `self.use_fused_vision_backbone == True`, uses both SigLIP and DINOv2 transformers to extract visual features
(otherwise uses SigLIP only). Allows multi-image inputs (but only for fused vision backbone).
Args:
pixel_values (torch.Tensor): Pixels for input image(s), (B, C, H, W).
"""
if self.num_images_in_input == 1:
if not self.use_fused_vision_backbone:
return self.featurizer(pixel_values)
# Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
return torch.cat([patches, patches_fused], dim=2)
else:
assert self.use_fused_vision_backbone, "Multi-image inputs require using fused backbone!"
# Split `pixel_values` into individual images (each with 6 channels: 3 for SigLIP + 3 for DINOv2)
images = torch.split(pixel_values, [6] * self.num_images_in_input, dim=1)
# Process each image and collect patches
all_patches = []
for img in images:
# Split each image further into two stacks of channels (each with 3 channels)
img_regular, img_fused = torch.split(img, [3, 3], dim=1)
# Get patches from both SigLIP and DINOv2 vision transformers
patches = self.featurizer(img_regular)
patches_fused = self.fused_featurizer(img_fused)
# Concatenate SigLIP and DINOv2 patches along the hidden dimension
combined_patches = torch.cat([patches, patches_fused], dim=2)
all_patches.append(combined_patches)
# Concatenate all patches along the patch dimension
return torch.cat(all_patches, dim=1)
# === Prismatic Projector (nn.Module) Definitions ===
class PrismaticProjector(nn.Module):
def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
super().__init__()
self.use_fused_vision_backbone = use_fused_vision_backbone
self.vision_dim, self.llm_dim = vision_dim, llm_dim
# Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
if not self.use_fused_vision_backbone:
self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
self.act_fn1 = nn.GELU()
else:
initial_projection_dim = 4 * vision_dim
self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
self.act_fn1 = nn.GELU()
self.act_fn2 = nn.GELU()
def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
if not self.use_fused_vision_backbone:
projected_features = self.fc1(img_patches)
projected_features = self.act_fn1(projected_features)
projected_features = self.fc2(projected_features)
else:
projected_features = self.fc1(img_patches)
projected_features = self.act_fn1(projected_features)
projected_features = self.fc2(projected_features)
projected_features = self.act_fn2(projected_features)
projected_features = self.fc3(projected_features)
return projected_features
# === Main HF Class Definitions ===
@dataclass
class PrismaticCausalLMOutputWithPast(ModelOutput):
"""Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
# Additions for VLMs
projector_features: Optional[torch.FloatTensor] = None
class PrismaticPreTrainedModel(PreTrainedModel):
config_class: PretrainedConfig = PrismaticConfig
base_model_prefix: str = "model"
supports_gradient_checkpointing: bool = True
_no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
_skip_keys_device_placement: str = "past_key_values"
_supports_flash_attn_2: bool = True
def _init_weights(self, module: nn.Module) -> None:
# Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
# => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
# https://github.com/TRI-ML/prismatic-vlms
std = (
self.config.initializer_range
if hasattr(self.config, "initializer_range")
else self.config.text_config.initializer_range
)
if hasattr(module, "class_embedding"):
module.class_embedding.data.normal_(mean=0.0, std=std)
if isinstance(module, (nn.Linear, nn.Conv2d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
@property
def _supports_sdpa(self) -> bool:
"""Check LLM supports SDPA Attention"""
return self.language_model._supports_sdpa
class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
def __init__(self, config: PrismaticConfig) -> None:
super().__init__(config)
# [Validation] Lightweight Validate on `config` Fields + Dependency Versions
if config.use_fused_vision_backbone is None:
raise ValueError("Missing config field `use_fused_vision_backbone`")
if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
raise NotImplementedError(
"TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
"if you urgently need support for latest TIMM versions."
)
if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
logger.warning(
f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
f"there might be inference-time regressions due to dependency changes. If in doubt, please"
f"use the above versions."
)
# Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
self.vision_backbone = PrismaticVisionBackbone(
config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
)
# Create Multimodal Projector
self.projector = PrismaticProjector(
config.use_fused_vision_backbone,
vision_dim=self.vision_backbone.embed_dim,
llm_dim=config.text_config.hidden_size,
)
# Instantiate LLM Backbone
self.language_model = AutoModelForCausalLM.from_config(
config.text_config, attn_implementation=config._attn_implementation
)
self.vocab_size = config.text_config.vocab_size
self.pad_token_id = config.pad_token_id
self.llm_dim = config.text_config.hidden_size
# HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
self.post_init()
# === `PreTrainedModel` Boilerplate ===
def get_input_embeddings(self) -> nn.Module:
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value: nn.Module) -> None:
self.language_model.set_input_embeddings(value)
def get_output_embeddings(self) -> nn.Module:
return self.language_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
self.language_model.set_output_embeddings(new_embeddings)
def get_decoder(self) -> nn.Module:
return self.language_model.get_decoder()
def set_decoder(self, decoder: nn.Module) -> None:
self.language_model.set_decoder(decoder)
def tie_weights(self) -> None:
self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
def resize_token_embeddings(
self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
) -> nn.Embedding:
updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
# Update config/instance variables
self.config.text_config.vocab_size = updated_embeddings.num_embeddings
self.vocab_size = updated_embeddings.num_embeddings
return updated_embeddings
def _replace_input_embeddings(self, input_embeddings, all_actions_mask, noisy_action_features):
"""
Replace embeddings in input_embeddings at positions where all_actions_mask is True
with embeddings from noisy_action_features, using vectorized operations.
Args:
input_embeddings: Tensor of shape (B, S, D)
all_actions_mask: Boolean tensor of shape (B, S)
noisy_action_features: Tensor of shape (B, K, D) where K is the number of True values in mask per sample
Returns:
Modified input_embeddings tensor
"""
# Clone input to avoid modifying the original tensor
new_input_embeddings = input_embeddings.clone()
# Create a tensor with the same shape of input_embeddings to hold the noisy action features
repositioned_noisy_action_features = torch.zeros_like(input_embeddings)
# Create batch indices for splicing
batch_indices = torch.arange(input_embeddings.shape[0], device=input_embeddings.device)
batch_indices = batch_indices.unsqueeze(1).expand(-1, noisy_action_features.shape[1])
# Get indices where mask is True for each sample
masked_indices = torch.stack([torch.where(mask)[0] for mask in all_actions_mask])
# Move the noisy action features into their correct positions
repositioned_noisy_action_features[batch_indices, masked_indices] = noisy_action_features
# Combine original input embeddings and noisy action embeddings using the mask
new_input_embeddings = torch.where(
all_actions_mask.unsqueeze(-1), repositioned_noisy_action_features, new_input_embeddings
)
return new_input_embeddings
def _process_action_masks(self, labels):
"""Helper to get action masks from labels"""
current_action_mask = get_current_action_mask(labels)
next_actions_mask = get_next_actions_mask(labels)
all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len)
return all_actions_mask
def _process_vision_features(self, pixel_values, language_embeddings=None, use_film=False):
"""Process vision features with optional FiLM conditioning"""
if use_film:
# FiLM: Infuse language inputs into visual features
patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D)
else:
patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D)
# Project patch embeddings into language embedding space
return self.projector(patch_features)
def _process_proprio_features(self, projected_patch_embeddings, proprio, proprio_projector):
"""Process proprioceptive features and append to vision features"""
if proprio_projector is not None and proprio is not None:
# projected_patch_embeddings: (bsz, num_patches * num_images, llm_dim)
# proprio: (bsz, proprio_dim) or (propro_dim,)
proprio = proprio.reshape(projected_patch_embeddings.shape[0], -1) # (bsz, proprio_dim)
proprio_features = proprio_projector(proprio) # (bsz, llm_dim)
proprio_features = proprio_features.unsqueeze(dim=1) # (bsz, 1, llm_dim)
# For simplicity, just append proprio token to the end of projected vision patch tokens
return torch.cat((projected_patch_embeddings, proprio_features), dim=1)
return projected_patch_embeddings
def _build_multimodal_attention(self, input_embeddings, projected_patch_embeddings, attention_mask):
"""Build multimodal embeddings and attention mask"""
# Update attention mask
projected_patch_attention_mask = None
if attention_mask is not None:
projected_patch_attention_mask = torch.full(
(projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
fill_value=True,
dtype=attention_mask.dtype,
device=attention_mask.device,
)
# Build multimodal embeddings & attention mask; insert embeddings after <BOS> token (1:)
multimodal_embeddings = torch.cat(
[input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
)
multimodal_attention_mask = None
if attention_mask is not None:
multimodal_attention_mask = torch.cat(
[attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
)
return multimodal_embeddings, multimodal_attention_mask
def _build_multimodal_labels(self, labels, projected_patch_embeddings):
"""Build multimodal labels with IGNORE_INDEX for patch embeddings"""
if labels is not None:
projected_patch_labels = torch.full(
(projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
fill_value=IGNORE_INDEX,
dtype=labels.dtype,
device=labels.device,
)
return torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
return None
# === Core Prismatic VLM `forward()` Logic ===
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_projector_features: Optional[bool] = None,
return_dict: Optional[bool] = None,
proprio=None,
proprio_projector=None,
noisy_actions=None,
noisy_action_projector=None,
diffusion_timestep_embeddings=None,
use_film: bool = False,
) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
"""Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_projector_features = output_projector_features if output_projector_features is not None else False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
use_cache = use_cache and not self.training
# Instantiate Placeholder for Projector Features
projected_patch_embeddings = None
# === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
if input_ids.shape[1] == 1:
assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
assert labels is None, "Unexpected key `labels` provided during cached generation!"
language_model_output = self.language_model(
input_ids=input_ids,
attention_mask=None,
position_ids=None,
past_key_values=past_key_values,
inputs_embeds=None,
labels=None,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# === Handle Unimodal Forward ===
elif pixel_values is None:
assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
language_model_output = self.language_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=None,
past_key_values=None,
inputs_embeds=None,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# === Handle Multimodal Forward ===
elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
assert past_key_values is None, "Unexpected key `past_key_values` provided during multimodal forward!"
# Get input embeddings (from language model embeddings)
input_embeddings = self.get_input_embeddings()(input_ids) # (B, seq_len, D)
# Extract action masks
all_actions_mask = self._process_action_masks(labels)
# Extract the language portion of the input embeddings (i.e. remove the action tokens portion)
language_embeddings = input_embeddings[~all_actions_mask].reshape(
input_embeddings.shape[0], -1, input_embeddings.shape[2]
) # (B, lang_seq_len, llm_dim)
# Get visual features
projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
# Add proprioceptive state if provided
projected_patch_embeddings = self._process_proprio_features(
projected_patch_embeddings, proprio, proprio_projector
)
# [Diffusion] Add diffusion timestep embedding if provided
if diffusion_timestep_embeddings is not None:
# For simplicity, just append diffusion timestep embedding to the end of projected vision patch tokens
projected_patch_embeddings = torch.cat(
(projected_patch_embeddings, diffusion_timestep_embeddings), dim=1
)
# Process action embeddings
if noisy_actions is not None:
# Get mask corresponding to all action tokens
all_actions_mask = self._process_action_masks(labels)
# Reshape noisy actions into individual action tokens
# noisy_actions: (B, chunk_len, action_dim) -> (B, chunk_len * action_dim, 1)
B = noisy_actions.shape[0]
noisy_actions = noisy_actions.reshape(B, -1).unsqueeze(-1)
# Project noisy action tokens into language model embedding space
noisy_action_features = noisy_action_projector(noisy_actions) # (B, chunk_len * action_dim, llm_dim)
# Replace embeddings of the action tokens with noisy action embeddings
input_embeddings = self._replace_input_embeddings(
input_embeddings, all_actions_mask, noisy_action_features
)
else:
# Replace the embeddings of the action tokens with zeros
# (Later on, the positional embeddings will be added to them)
all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
input_embeddings = input_embeddings * ~all_actions_mask
# Build multimodal embeddings & attention mask
multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
input_embeddings, projected_patch_embeddings, attention_mask
)
# Build labels for multimodal sequence if needed
multimodal_labels = self._build_multimodal_labels(labels, projected_patch_embeddings)
# Dispatch to language model
language_model_output = self.language_model(
input_ids=None,
attention_mask=multimodal_attention_mask,
position_ids=None,
past_key_values=None,
inputs_embeds=multimodal_embeddings,
labels=multimodal_labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# === Otherwise =>> Assume Invalid! ===
elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
else:
raise ValueError(
"Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
f"=> `input_ids` = {input_ids is not None}\n"
f"=> `attention_mask` = {attention_mask is not None}\n"
f"=> `pixel_values` = {pixel_values is not None}\n"
f"=> `labels` = {labels is not None}\n"
f"=> `input_embeds` = {inputs_embeds is not None}\n"
f"=> `past_key_values` = {past_key_values is not None}\n"
f"=> `use_cache` = {use_cache}"
)
# Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
if not return_dict:
if output_projector_features and (projected_patch_embeddings is not None):
return *language_model_output, projected_patch_embeddings
return language_model_output
return PrismaticCausalLMOutputWithPast(
loss=language_model_output.loss,
logits=language_model_output.logits,
past_key_values=language_model_output.past_key_values,
hidden_states=language_model_output.hidden_states,
attentions=language_model_output.attentions,
projector_features=projected_patch_embeddings,
)
# === GenerationMixin Methods ===
def prepare_inputs_for_generation(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**kwargs: str,
) -> Dict[str, torch.Tensor]:
"""Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
(inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
):
raise ValueError("Generation with batch size > 1 is not currently supported!")
# Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
if past_key_values is not None:
input_ids = input_ids[:, -1:]
# If `input_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"input_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
# Make sure `pixel_values` are preserved in `model_inputs`
model_inputs.update(
{
"attention_mask": attention_mask,
"pixel_values": pixel_values,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
}
)
return model_inputs
# Defer to Language Model (all handle this differently, with different return types)
def _reorder_cache(self, *args, **kwargs) -> Any:
return self.language_model._reorder_cache(*args, **kwargs)
class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
config_class: PretrainedConfig = OpenVLAConfig
def __init__(self, config: OpenVLAConfig) -> None:
super().__init__(config)
self.norm_stats = config.norm_stats
# Compute action bins
self.bins = np.linspace(-1, 1, config.n_action_bins)
self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
# Compute vocab size for de-tokenization -- revert added "multiple of"
self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
def _prepare_input_for_action_prediction(self, input_ids, attention_mask):
"""Prepares input for action prediction by adding necessary tokens"""
# Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens
placeholder_action_token_ids = (
torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
)
input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
# Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX
input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
# Extend the attention mask to fit the new shape of input
# Note: Only batch size == 1 supported right now
mask_extension = (
torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
.to(attention_mask.device)
.to(attention_mask.dtype)
)
attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
return input_ids, attention_mask
def _prepare_labels_for_action_prediction(self, labels, input_ids):
"""Creates labels tensor for action prediction if not provided"""
# Extend labels tensor with fake action labels
ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1
labels_extension = (
torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
* ARBITRARY_ACTION_TOKEN_IDX
)
labels = torch.cat([labels, labels_extension], dim=-1)
# Replace last label token with stop token
labels[:, -1] = STOP_INDEX
return labels
def _unnormalize_actions(self, normalized_actions, unnorm_key=None):
"""Unnormalize actions using dataset statistics"""
action_norm_stats = self.get_action_stats(unnorm_key)
if ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS:
mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["min"], dtype=bool))
action_high, action_low = np.array(action_norm_stats["max"]), np.array(action_norm_stats["min"])
elif ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS_Q99:
mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
else:
raise ValueError("Unsupported action/proprio normalization type detected!")
actions = np.where(
mask,
0.5 * (normalized_actions + 1) * (action_high - action_low + 1e-8) + action_low,
normalized_actions,
)
return actions
def _run_diffusion_prediction(
self,
input_embeddings,
all_actions_mask,
noise,
action_head,
projected_patch_embeddings,
labels,
attention_mask,
NUM_PATCHES,
NUM_PROMPT_TOKENS,
noisy_action_projector,
):
"""Run diffusion-based action prediction"""
# Set diffusion timestep values
action_head.noise_scheduler.set_timesteps(action_head.num_diffusion_steps)
# Clone embedding for reuse in each timestep
orig_projected_patch_embeddings = projected_patch_embeddings.clone()
curr_noisy_actions = noise
# Reverse diffusion: Iteratively denoise to generate action prediction
for t in action_head.noise_scheduler.timesteps:
# Get diffusion model's noise prediction (conditioned on VLA latent embedding, current noisy action
# embedding, and diffusion timestep embedding)
timesteps = torch.Tensor([t]).to(labels.device)
diffusion_timestep_embeddings = (
action_head.time_encoder(timesteps).to(curr_noisy_actions.dtype).to(curr_noisy_actions.device)
) # (B, llm_dim)
diffusion_timestep_embeddings = diffusion_timestep_embeddings.unsqueeze(1) # (B, 1, llm_dim)
# [Diffusion] Replace the embeddings of the action tokens with noisy actions
# (Later on, the positional embeddings will be added to them)
# For simplicity, append diffusion timestep embedding to the end of projected vision tokens
projected_patch_embeddings = torch.cat(
(orig_projected_patch_embeddings, diffusion_timestep_embeddings), dim=1
)
# Reshape and project noisy actions into language embedding space
B = curr_noisy_actions.shape[0]
orig_curr_noisy_actions_shape = curr_noisy_actions.shape
curr_noisy_actions = curr_noisy_actions.reshape(B, -1).unsqueeze(-1)
noisy_action_features = noisy_action_projector(curr_noisy_actions)
curr_noisy_actions = curr_noisy_actions.reshape(orig_curr_noisy_actions_shape)
# Replace action token embeddings with noisy action embeddings
input_embeddings = self._replace_input_embeddings(
input_embeddings.clone(), all_actions_mask, noisy_action_features
)
# Build multimodal embeddings and attention mask
multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
input_embeddings, projected_patch_embeddings, attention_mask
)
# Forward pass through language model
language_model_output = self.language_model(
input_ids=None,
attention_mask=multimodal_attention_mask,
position_ids=None,
past_key_values=None,
inputs_embeds=multimodal_embeddings,
labels=None,
use_cache=None,
output_attentions=False,
output_hidden_states=True,
return_dict=True,
)
# Extract hidden states for action portion of response
last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
actions_hidden_states = last_hidden_states[
:,
NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
:,
] # (B, act_chunk_len, D)
# Predict noise and update noisy actions: x_t -> x_{t-1}
noise_pred = action_head.predict_noise(actions_hidden_states)
curr_noisy_actions = action_head.noise_scheduler.step(noise_pred, t, curr_noisy_actions).prev_sample
curr_noisy_actions = curr_noisy_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
# Return final actions
return curr_noisy_actions.float().cpu().detach().numpy(), actions_hidden_states
def _regression_or_discrete_prediction(
self,
input_embeddings,
all_actions_mask,
projected_patch_embeddings,
attention_mask,
labels,
NUM_PATCHES,
NUM_PROMPT_TOKENS,
action_head=None,
):
"""Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
# Zero out action token embeddings
all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
input_embeddings = input_embeddings * ~all_actions_mask
# Build multimodal embeddings and attention mask
multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention(
input_embeddings, projected_patch_embeddings, attention_mask
)
# Forward pass through language model
language_model_output = self.language_model(
input_ids=None,
attention_mask=multimodal_attention_mask,
position_ids=None,
past_key_values=None,
inputs_embeds=multimodal_embeddings,
labels=None,
use_cache=None,
output_attentions=False,
output_hidden_states=True,
return_dict=True,
)
# Extract hidden states for action tokens
last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D)
actions_hidden_states = last_hidden_states[
:,
NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
:,
] # (B, act_chunk_len, D)
# Handle different prediction methods
if action_head is not None:
# L1 regression prediction
normalized_actions = action_head.predict_action(actions_hidden_states)
normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
normalized_actions = normalized_actions.float().cpu().detach().numpy()
else:
# Discrete token-based prediction
predicted_action_token_ids = (
language_model_output.logits[
:,
NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK,
]
.argmax(dim=2)
.cpu()
.numpy()
)
discretized_actions = self.vocab_size - predicted_action_token_ids
discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
normalized_actions = self.bin_centers[discretized_actions]
normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
return normalized_actions, actions_hidden_states
def predict_action(
self,
input_ids: Optional[torch.LongTensor] = None,
unnorm_key: Optional[str] = None,
proprio=None,
proprio_projector=None,
action_head=None,
noisy_action_projector=None,
use_film: bool = False,
**kwargs: str,
) -> np.ndarray:
"""Predict actions from input sequence, with options for different prediction methods.
Args:
input_ids: Input token ids
unnorm_key: Key for unnormalization statistics
proprio: Proprioceptive features
proprio_projector: Projector for proprioceptive features
action_head: Optional head for L1 regression or diffusion-based prediction
noisy_action_projector: Projector for noisy actions in diffusion-based prediction
use_film: Whether to use FiLM conditioning
**kwargs: Additional arguments including pixel_values and attention_mask
Returns:
Tuple of (unnormalized_actions, action_hidden_states)
"""
# If the special empty token ('') does not already appear after the colon (':') token in the prompt
# (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
if not torch.all(input_ids[:, -1] == 29871):
input_ids = torch.cat(
(input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
)
pixel_values = kwargs["pixel_values"]
attention_mask = kwargs["attention_mask"]
# Create fake labels tensor (needed for action mask)
labels = input_ids.clone()
labels[:] = IGNORE_INDEX
# Get number of tokens in prompt (excluding the start token)
NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token
# Prepare inputs by adding necessary tokens
input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask)
# Update labels tensor for action mask computation later
labels = self._prepare_labels_for_action_prediction(labels, input_ids)
# Get input embeddings and action masks
input_embeddings = self.get_input_embeddings()(input_ids)
all_actions_mask = self._process_action_masks(labels)
# Extract language embeddings
language_embeddings = input_embeddings[~all_actions_mask].reshape(
input_embeddings.shape[0], -1, input_embeddings.shape[2]
)
# Process vision features
projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film)
# Add proprioceptive features if provided
use_proprio = proprio_projector is not None and proprio is not None
if use_proprio:
proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype)
projected_patch_embeddings = self._process_proprio_features(
projected_patch_embeddings, proprio, proprio_projector
)
# Use diffusion if provided, otherwise use regression or discrete prediction
use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler")
# Calculate number of patches (including proprio token and/or diffusion timestep embedding if present)
NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input()
if use_proprio:
NUM_PATCHES += 1
if use_diffusion:
NUM_PATCHES += 1
if use_diffusion:
# Sample random noise with shape equal to output action, used as the starting state for reverse diffusion
noise = torch.randn(
size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype
)
# Run diffusion-based prediction
normalized_actions, actions_hidden_states = self._run_diffusion_prediction(
input_embeddings,
all_actions_mask,
noise,
action_head,
projected_patch_embeddings,
labels,
attention_mask,
NUM_PATCHES,
NUM_PROMPT_TOKENS,
noisy_action_projector,
)
else:
# Run regression or discrete token-based prediction
normalized_actions, actions_hidden_states = self._regression_or_discrete_prediction(
input_embeddings,
all_actions_mask,
projected_patch_embeddings,
attention_mask,
labels,
NUM_PATCHES,
NUM_PROMPT_TOKENS,
action_head,
)
# Unnormalize predicted actions
actions = self._unnormalize_actions(normalized_actions, unnorm_key)
return actions, actions_hidden_states
@staticmethod
def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
"""Validate and resolve the unnormalization key for action statistics"""
if unnorm_key is None:
assert len(norm_stats) == 1, (
f"Your model was trained on more than one dataset, "
f"please pass a `unnorm_key` from the following options to choose the statistics "
f"used for un-normalizing actions: {norm_stats.keys()}"
)
unnorm_key = next(iter(norm_stats.keys()))
assert unnorm_key in norm_stats, (
f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
f"please choose from: {norm_stats.keys()}"
)
return unnorm_key
def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
"""Get the dimensionality of the policy's action space."""
unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
return len(self.norm_stats[unnorm_key]["action"]["min"])
def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
"""Get all the logged statistics for the given dataset."""
unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
return self.norm_stats[unnorm_key]["action"]