""" 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 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"]