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Non-Production Environment may include, but is not limited to, any setting, use case, or application for research, development, testing, quality assurance, training, internal evaluation (other than any internal usage by employees in the context of the company’s business activities), and demonstration purposes. # # **“Outputs”**: means any content generated by the operation of the Perceptron Models or the Derivatives from a prompt (i.e., text instructions) provided by users. For the avoidance of doubt, Outputs do not include any components of a Perceptron Models, such as any fine-tuned versions of the Perceptron Models, the weights, or parameters. # # **“Personal”**: means any use of a Perceptron Model or a Derivative that is (i) solely for personal, non-profit and non-commercial purposes and (ii) not directly or indirectly connected to any commercial activities, business operations, or employment responsibilities. For illustration purposes, Personal use of a Model or a Derivative does not include any usage by individuals employed in companies in the context of their daily tasks, any activity that is intended to generate revenue, or that is performed on behalf of a commercial entity. # # **“You”**: means the individual or entity entering into this Agreement with Perceptron, Inc.. from __future__ import annotations import copy import math import re from collections import defaultdict from typing import Any, Callable, Optional, Sequence, Union import PIL.Image import torch import torch.nn as nn import torch.nn.functional as F from transformers import ( AutoImageProcessor, AutoModel, AutoTokenizer, BatchFeature, Cache, Qwen3Config, Qwen3ForCausalLM, Qwen3PreTrainedModel, ) from transformers.cache_utils import SlidingWindowCache, StaticCache from transformers.generation.utils import GenerationMixin from transformers.image_processing_utils_fast import ( BaseImageProcessorFast, SizeDict, group_images_by_shape, reorder_images, DefaultFastImageProcessorKwargs, ) from transformers.image_utils import ( ChannelDimension, PILImageResampling, ) from transformers.modeling_attn_mask_utils import AttentionMaskConverter from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS from transformers.models.qwen2.tokenization_qwen2 import Qwen2Tokenizer from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig from transformers.models.siglip2.modeling_siglip2 import ( Siglip2Attention, Siglip2Encoder as HFSiglip2Encoder, Siglip2EncoderLayer as HFSiglip2EncoderLayer, Siglip2VisionEmbeddings as HFSiglip2VisionEmbeddings, ) from transformers.processing_utils import ProcessorMixin, ProcessingKwargs, Unpack from transformers.tokenization_utils import TensorType from transformers.utils import auto_docstring from transformers.utils.generic import can_return_tuple # Vision preprocessing constants from transformers.utils.constants import IMAGENET_STANDARD_MEAN as VISION_MEAN from transformers.utils.constants import IMAGENET_STANDARD_STD as VISION_STD from transformers.utils.import_utils import is_torchdynamo_compiling try: from genesis.public.tensorstream.tensor_stream import ( Event, Stream, TensorStream, TextType, VisionType, create_stream, group_streams, ) from genesis.public.tensorstream.tensor_stream_utils import ( compute_mrope_pos_tensor, modality_mask, reconstruct_tensor_stream_from_compact_dict, tensor_stream_token_view, ) from genesis.public.tensorstream.tensor_stream_utils import ( slice as ts_slice, ) except ModuleNotFoundError as exc: # pragma: no cover - import guard raise ModuleNotFoundError( "genesis.public.tensorstream is required for the Isaac HuggingFace integration. " "Ensure the TensorStream package is installed and on PYTHONPATH." ) from exc _ORIGINAL_ATTENTION_FUNCTIONS: dict[str, Callable[..., tuple[torch.Tensor, Optional[torch.Tensor]]]] = {} for _attn_name in ("flash_attention_2", "sdpa", "eager"): if _attn_name in ALL_ATTENTION_FUNCTIONS: _ORIGINAL_ATTENTION_FUNCTIONS[_attn_name] = ALL_ATTENTION_FUNCTIONS[_attn_name] class IsaacVisionConfig(Siglip2VisionConfig): """Vision configuration for Isaac with Pixel Shuffle support. Extends Siglip2VisionConfig with additional fields for pixel shuffle. Args: pixel_shuffle_scale_factor (`int`, *optional*, defaults to 1): Spatial factor applied before pixel shuffle reduces the resolution. num_patches (`int`, *optional*, defaults to 256): Maximum number of learnable positional embeddings to initialize. """ model_type = "isaac_vision" base_config_key = "vision_config" _attn_implementation: str | None = None def __init__( self, pixel_shuffle_scale_factor: int = 1, num_patches: int = 256, **kwargs, ): super().__init__(**kwargs) # Add our custom fields self.pixel_shuffle_scale_factor = pixel_shuffle_scale_factor self.num_patches = num_patches if self._attn_implementation is None: self._attn_implementation = "flash_attention_2" class IsaacImageProcessorKwargs(DefaultFastImageProcessorKwargs, total=False): patch_size: int | None max_num_patches: int | None min_num_patches: int | None pixel_shuffle_scale: int | None class IsaacProcessorKwargs(ProcessingKwargs, total=False): images_kwargs: IsaacImageProcessorKwargs # Ensure python<=3.9 compatibility with TypedDict overrides. IsaacProcessorKwargs.__annotations__["images_kwargs"] = IsaacImageProcessorKwargs @auto_docstring class IsaacImageProcessorFast(BaseImageProcessorFast): MAX_PIXELS = 60_000_000 # 60‑megapixel ceiling ≈ 8200 × 7300 px r"""Fast torch-based image processor for Isaac vision inputs.""" resample = PILImageResampling.BILINEAR model_input_names = ["patches", "token_grids"] valid_kwargs = IsaacImageProcessorKwargs unused_kwargs = ["size", "do_center_crop", "crop_size"] do_resize = True size: SizeDict | None = None default_to_square: bool | None = None do_center_crop = False crop_size: SizeDict | None = None patch_size: int | None = 16 max_num_patches: int | None = 256 min_num_patches: int | None = None pixel_shuffle_scale: int | None = 1 do_pad = False pad_size: SizeDict | None = None do_rescale = True rescale_factor = 1 / 255 do_normalize = True image_mean = list(VISION_MEAN) image_std = list(VISION_STD) do_convert_rgb = True return_tensors = None data_format = ChannelDimension.FIRST input_data_format = None device = None disable_grouping = False size_divisor: int | None = None def __init__( self, **kwargs: Unpack[IsaacImageProcessorKwargs], ) -> None: super().__init__(**kwargs) pixel_shuffle_scale = 1 if self.pixel_shuffle_scale is None else int(self.pixel_shuffle_scale) if pixel_shuffle_scale < 1: raise ValueError("`pixel_shuffle_scale` must be >= 1") self.pixel_shuffle_scale = pixel_shuffle_scale def _validate_preprocess_kwargs(self, **kwargs): # Allow callers to omit resize-related placeholders that BaseImageProcessorFast checks for. kwargs.pop("do_resize", None) kwargs.pop("size", None) kwargs.pop("do_center_crop", None) kwargs.pop("crop_size", None) kwargs.pop("disable_grouping", None) return super()._validate_preprocess_kwargs(**kwargs) def resize( self, image: "torch.Tensor", size: SizeDict, interpolation: Optional[Any] = None, antialias: bool = True, **kwargs, ) -> torch.Tensor: if size.height is None or size.width is None: raise ValueError("IsaacImageProcessorFast requires explicit `height` and `width` when resizing.") resize_mode: Any = interpolation if hasattr(resize_mode, "value"): resize_mode = resize_mode.value elif hasattr(resize_mode, "name"): resize_mode = resize_mode.name.lower() elif resize_mode is None: resize_mode = "bilinear" if isinstance(resize_mode, str): mode_key = resize_mode.lower() else: mode_key = resize_mode resize_kwargs: dict[str, Any] = {} if mode_key in {"linear", "bilinear", "bicubic", "trilinear"}: resize_kwargs["align_corners"] = False return F.interpolate( image, size=(size.height, size.width), mode=resize_mode, **resize_kwargs, ) def _preprocess( self, images: list["torch.Tensor"], do_resize: bool, size: Optional[SizeDict], interpolation: Optional[Any], do_center_crop: bool, crop_size: Optional[SizeDict], do_rescale: Optional[bool], rescale_factor: Optional[float], do_normalize: Optional[bool], image_mean: Optional[Union[float, Sequence[float]]], image_std: Optional[Union[float, Sequence[float]]], disable_grouping: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, do_pad: Optional[bool] = None, pad_size: Optional[SizeDict] = None, *, patch_size: int | None = None, max_num_patches: int | None = None, min_num_patches: int | None = None, pixel_shuffle_scale: int | None = None, **kwargs, ) -> BatchFeature: if do_center_crop: raise ValueError("`do_center_crop` is not supported by IsaacImageProcessorFast.") if do_pad: raise ValueError("`do_pad` is not supported by IsaacImageProcessorFast.") grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) processed_patches_grouped: dict[tuple[int, ...], torch.Tensor] = {} token_grids_grouped: dict[tuple[int, ...], torch.Tensor] = {} virtual_dims_grouped: dict[tuple[int, ...], torch.Tensor] = {} real_dims_grouped: dict[tuple[int, ...], torch.Tensor] = {} for shape, stacked_images in grouped_images.items(): if stacked_images.ndim != 4: raise ValueError("Expected batched channel-first image tensors.") batch_size, channels, original_height, original_width = stacked_images.shape if bool(self.do_convert_rgb) and channels == 1: stacked_images = stacked_images.repeat(1, 3, 1, 1) channels = 3 if original_height * original_width > self.MAX_PIXELS: raise ValueError( f"Image (w={original_width}, h={original_height}) > MAX=`{self.MAX_PIXELS}`" ) target_height, target_width = get_image_size_for_max_num_patches( original_height, original_width, patch_size, max_num_patches, min_num_patches=min_num_patches, pixel_shuffle_scale=pixel_shuffle_scale, ) if do_resize: resize_size = SizeDict(height=target_height, width=target_width) image_batch = self.resize( image=stacked_images, size=resize_size, interpolation=interpolation, ) else: if ((original_height % patch_size) != 0) or ((original_width % patch_size) != 0): raise ValueError( "Image dimensions must be divisible by patch_size when resize is disabled." ) image_batch = stacked_images target_height, target_width = original_height, original_width if do_rescale: image_batch = self.rescale_and_normalize( image_batch, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, ) nhwc_images = image_batch.permute(0, 2, 3, 1) nhwc_images = _compute_residual_p_frames(nhwc_images, is_p_frame=[False] * batch_size) patches = patchify_vision(nhwc_images, patch_size=patch_size) _, height_tokens, width_tokens, _ = patches.shape token_grid = torch.tensor( [height_tokens, width_tokens], dtype=torch.long, device=patches.device, ).unsqueeze(0).repeat(batch_size, 1) real_dim = torch.tensor( [1, height_tokens, width_tokens], dtype=torch.long, device=patches.device, ).unsqueeze(0).repeat(batch_size, 1) if pixel_shuffle_scale > 1: if (height_tokens % pixel_shuffle_scale) or (width_tokens % pixel_shuffle_scale): raise ValueError( "Spatial dimensions must be divisible by pixel_shuffle_scale when pixel shuffle is enabled." ) virtual_height = height_tokens // pixel_shuffle_scale virtual_width = width_tokens // pixel_shuffle_scale else: virtual_height = height_tokens virtual_width = width_tokens virtual_dim = torch.tensor( [1, virtual_height, virtual_width], dtype=torch.long, device=patches.device, ).unsqueeze(0).repeat(batch_size, 1) processed_patches_grouped[shape] = patches token_grids_grouped[shape] = token_grid virtual_dims_grouped[shape] = virtual_dim real_dims_grouped[shape] = real_dim patches_slices = reorder_images(processed_patches_grouped, grouped_images_index) token_grid_slices = reorder_images(token_grids_grouped, grouped_images_index) virtual_dim_slices = reorder_images(virtual_dims_grouped, grouped_images_index) real_dim_slices = reorder_images(real_dims_grouped, grouped_images_index) patches_tensor = torch.stack(patches_slices, dim=0) token_grids_tensor = torch.stack(token_grid_slices, dim=0) virtual_dims_tensor = torch.stack(virtual_dim_slices, dim=0) real_dims_tensor = torch.stack(real_dim_slices, dim=0) return BatchFeature( data={ "patches": patches_tensor, "token_grids": token_grids_tensor, "virtual_pixel_size": virtual_dims_tensor, "real_pixel_size": real_dims_tensor, }, tensor_type=return_tensors, ) def _max_from_cu(cu: torch.Tensor | None, fallback: int) -> int: """Helper to compute max sequence length from cumulative sequence lengths.""" if cu is None or len(cu) < 2: return fallback return int((cu[1:] - cu[:-1]).max().item()) def build_document_attention_mask( cu_seqlens: torch.Tensor | None, total_tokens: int, dtype: torch.dtype, device: torch.device, ) -> torch.Tensor | None: """Creates an additive attention mask that blocks cross-document attention.""" if cu_seqlens is None: return None if cu_seqlens.numel() < 2: return None seq_sizes = (cu_seqlens[1:] - cu_seqlens[:-1]).long() if seq_sizes.numel() == 0: return None seg_ids = torch.repeat_interleave(torch.arange(seq_sizes.numel(), device=device), seq_sizes) block_mask = seg_ids[:, None] != seg_ids[None, :] additive_mask = torch.zeros((total_tokens, total_tokens), dtype=dtype, device=device) additive_mask.masked_fill_(block_mask, float("-inf")) return additive_mask.view(1, 1, total_tokens, total_tokens) def ensure_document_attention_mask( attention_mask: Optional[torch.Tensor], cu_seqlens: Optional[torch.Tensor], total_tokens: int, dtype: torch.dtype, device: torch.device, ) -> Optional[torch.Tensor]: if attention_mask is not None or cu_seqlens is None: return attention_mask return build_document_attention_mask( cu_seqlens=cu_seqlens, total_tokens=total_tokens, dtype=dtype, device=device, ) def flash_attention_document_mask_forward( module: torch.nn.Module, q_lhd: torch.Tensor, # (L, H, D) k_lhd: torch.Tensor, # (L, H, D) v_lhd: torch.Tensor, # (L, H, D) attention_mask: torch.Tensor | None = None, # unused for FA path dropout: float = 0.0, scaling: float | None = None, cum_seq_q: torch.Tensor | None = None, cum_seq_k: torch.Tensor | None = None, max_seqlen: int | None = None, is_causal: bool = False, **kwargs, ) -> tuple[torch.Tensor, None]: """FlashAttention that consumes (L, H, D) directly to avoid layout churn.""" L, H, D = q_lhd.shape # Compute max block length once (honor caller when provided) if max_seqlen is not None: max_q = max_k = int(max_seqlen) else: max_q = _max_from_cu(cum_seq_q, L) max_k = _max_from_cu(cum_seq_k, L) # Ensure contiguity only if needed if not q_lhd.is_contiguous(): q_lhd = q_lhd.contiguous() if not k_lhd.is_contiguous(): k_lhd = k_lhd.contiguous() if not v_lhd.is_contiguous(): v_lhd = v_lhd.contiguous() out_lhd, *_ = torch.ops.aten._flash_attention_forward( query=q_lhd, # (L, H, D) key=k_lhd, # (L, H, D) value=v_lhd, # (L, H, D) cum_seq_q=cum_seq_q, cum_seq_k=cum_seq_k, max_q=max_q, max_k=max_k, dropout_p=dropout, is_causal=is_causal, return_debug_mask=False, scale=scaling, window_size_left=-1, window_size_right=-1, alibi_slopes=None, ) return out_lhd, None # (L, H, D) def sdpa_document_mask_forward( q_lhd: torch.Tensor, # (L, H, D) k_lhd: torch.Tensor, # (L, H, D) v_lhd: torch.Tensor, # (L, H, D) dropout: float, scaling: float | None, attention_mask: torch.Tensor | None = None, cu_seqlens: torch.Tensor | None = None, ) -> torch.Tensor: """SDPA with block-diagonal masking for variable-length sequences.""" L, H, D = q_lhd.shape # Transpose to (1, H, L, D) format for SDPA Q = q_lhd.permute(1, 0, 2).unsqueeze(0) K = k_lhd.permute(1, 0, 2).unsqueeze(0) V = v_lhd.permute(1, 0, 2).unsqueeze(0) # Build block-diagonal mask for variable-length sequences attn_mask = attention_mask if attn_mask is None: attn_mask = build_document_attention_mask( cu_seqlens=cu_seqlens, total_tokens=L, dtype=q_lhd.dtype, device=q_lhd.device, ) if attn_mask is not None and attn_mask.dtype != Q.dtype: attn_mask = attn_mask.to(Q.dtype) Y = F.scaled_dot_product_attention(Q, K, V, attn_mask=attn_mask, dropout_p=dropout, scale=scaling) return Y.squeeze(0).permute(1, 0, 2) # Back to (L, H, D) class IsaacVisionEmbeddings(HFSiglip2VisionEmbeddings): """Adapter around SigLIP2 vision embeddings that consumes packed patch sequences.""" def __init__(self, config: IsaacVisionConfig): super().__init__(config) def forward(self, seq_patches: torch.Tensor, spatial_shapes: torch.Tensor) -> torch.Tensor: packed_pixel_values, seq_lengths = self._pack_to_batch(seq_patches, spatial_shapes) if packed_pixel_values is None: return seq_patches.new_zeros((0, self.embed_dim)) embeddings = super().forward(packed_pixel_values, spatial_shapes) return self._unpack_from_batch(embeddings, seq_lengths) def _pack_to_batch( self, seq_patches: torch.Tensor, spatial_shapes: torch.Tensor, ) -> tuple[torch.Tensor | None, torch.Tensor]: if seq_patches.ndim != 2: raise ValueError("`seq_patches` is expected to be 2D (total_patches, patch_dim).") if spatial_shapes.ndim != 2 or spatial_shapes.size(-1) != 2: raise ValueError("`spatial_shapes` must have shape (num_images, 2) with (height_tokens, width_tokens).") seq_lengths = spatial_shapes.long().prod(dim=-1) total_patches = int(seq_lengths.sum().item()) if total_patches != seq_patches.size(0): raise ValueError( "Mismatch between packed patches and spatial shapes: got " f"{seq_patches.size(0)} patches but spatial shapes imply {total_patches}." ) batch_size = spatial_shapes.size(0) if batch_size == 0: return None, seq_lengths max_length = int(seq_lengths.max().item()) patch_dim = seq_patches.size(-1) device = seq_patches.device packed_pixel_values = seq_patches.new_zeros((batch_size, max_length, patch_dim), device=device) start = 0 for batch_idx, length in enumerate(seq_lengths.tolist()): if length == 0: continue end = start + length packed_pixel_values[batch_idx, :length] = seq_patches[start:end] start = end return packed_pixel_values, seq_lengths def _unpack_from_batch(self, embeddings: torch.Tensor, seq_lengths: torch.Tensor) -> torch.Tensor: output_chunks: list[torch.Tensor] = [] for batch_idx, length in enumerate(seq_lengths.tolist()): if length == 0: continue output_chunks.append(embeddings[batch_idx, :length]) if not output_chunks: return embeddings.new_zeros((0, embeddings.size(-1))) return torch.cat(output_chunks, dim=0) class IsaacVisionAttention(Siglip2Attention): """Custom attention that supports variable-length sequences with flash attention.""" ATTENTION_KEY_MAP: dict[str, str] = { "flash_attention_2": "isaac_flash_attention_2", "flash_attention_3": "isaac_flash_attention_3", "isaac_flash_attention_2": "isaac_flash_attention_2", "isaac_flash_attention_3": "isaac_flash_attention_3", "sdpa": "isaac_sdpa", "isaac_sdpa": "isaac_sdpa", "eager": "isaac_eager", "isaac_eager": "isaac_eager", } def __init__(self, vision_config): super().__init__(vision_config) self.vision_config = vision_config self._variable_length_metadata = None def _variable_length_context(self, *, cu_seqlens=None, max_seqlen=None): """Store packed-sequence metadata for the next forward call.""" self._variable_length_metadata = (cu_seqlens, max_seqlen) def _consume_variable_length_metadata(self): if self._variable_length_metadata is None: return None, None cu_seqlens, max_seqlen = self._variable_length_metadata self._variable_length_metadata = None return cu_seqlens, max_seqlen def forward(self, hidden_states, attention_mask=None, **kwargs): cu_seqlens = kwargs.pop("cu_seqlens", None) max_seqlen = kwargs.pop("max_seqlen", None) kwargs.pop("output_attentions", None) kwargs.pop("output_hidden_states", None) kwargs.pop("return_dict", None) if kwargs: unexpected = ', '.join(sorted(kwargs)) raise TypeError(f'Unexpected kwargs for IsaacVisionAttention.forward: {unexpected}') cached_cu, cached_max = self._consume_variable_length_metadata() if cu_seqlens is None: cu_seqlens = cached_cu if max_seqlen is None: max_seqlen = cached_max # Expect packed sequences with batch_size == 1 batch_size, L, _ = hidden_states.shape if batch_size != 1: raise ValueError("packed variable-length attention expects batch_size=1") x = hidden_states[0] # (L, E) H = self.num_heads D = self.head_dim p_drop = self.dropout if self.training else 0.0 # Project and reshape to (L, H, D) q = self.q_proj(x).view(L, H, D) k = self.k_proj(x).view(L, H, D) v = self.v_proj(x).view(L, H, D) attn_impl = getattr(self.vision_config, "_attn_implementation", "flash_attention_3") attn_mask = ensure_document_attention_mask( attention_mask, cu_seqlens, L, q.dtype, q.device, ) resolved_key = self.ATTENTION_KEY_MAP.get(attn_impl) attention_fn = ALL_ATTENTION_FUNCTIONS.get(resolved_key) if resolved_key is not None else None if attention_fn is None: raise ValueError(f"Attention implementation {attn_impl} not found.") query_states = q.transpose(0, 1).unsqueeze(0) key_states = k.transpose(0, 1).unsqueeze(0) value_states = v.transpose(0, 1).unsqueeze(0) attention_kwargs: dict[str, Any] = { "dropout": p_drop, "scaling": self.scale, "is_causal": False, } if cu_seqlens is not None: attention_kwargs["cu_seq_lens_q"] = cu_seqlens attention_kwargs["cu_seq_lens_k"] = cu_seqlens if max_seqlen is not None: attention_kwargs["max_length_q"] = max_seqlen attention_kwargs["max_length_k"] = max_seqlen attn_output, _ = attention_fn( self, query_states, key_states, value_states, attn_mask, **attention_kwargs, ) y_lhd = attn_output.squeeze(0).permute(1, 0, 2).contiguous() # Merge heads and project y = self.out_proj(y_lhd.reshape(L, self.embed_dim)) return y.unsqueeze(0), None # (1, L, E) class IsaacVisionEncoderLayer(HFSiglip2EncoderLayer): """Isaac vision encoder layer with variable-length attention.""" def __init__(self, vision_config: IsaacVisionConfig): super().__init__(vision_config) self.self_attn = IsaacVisionAttention(vision_config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, cu_seqlens: Optional[torch.Tensor] = None, max_seqlen: Optional[int] = None, output_attentions: bool = False, output_hidden_states: Optional[bool] = None, ): if cu_seqlens is not None or max_seqlen is not None: self.self_attn._variable_length_context( cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, ) attention_mask = ensure_document_attention_mask( attention_mask, cu_seqlens, hidden_states.size(1), hidden_states.dtype, hidden_states.device, ) return super().forward( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, ) class IsaacVisionEncoder(HFSiglip2Encoder): """Encoder using Isaac encoder layers with variable-length attention support.""" def __init__(self, config: IsaacVisionConfig): super().__init__(config) self.layers = nn.ModuleList([IsaacVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]) def __variable_length_context(self, cu_seqlens, max_seqlen) -> None: if cu_seqlens is None and max_seqlen is None: return for layer in self.layers: if isinstance(layer, IsaacVisionEncoderLayer): layer.self_attn._variable_length_context( cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, ) @can_return_tuple def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, cu_seqlens: Optional[torch.Tensor] = None, max_seqlen: Optional[int] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): self.__variable_length_context(cu_seqlens, max_seqlen) attention_mask = ensure_document_attention_mask( attention_mask, cu_seqlens, inputs_embeds.size(1), inputs_embeds.dtype, inputs_embeds.device, ) return super().forward( inputs_embeds, attention_mask=attention_mask, output_attentions=output_attentions, #output_hidden_states=output_hidden_states, #return_dict=return_dict, ) def _isaac_flash_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], dropout: float = 0.0, scaling: Optional[float] = None, is_causal: bool = False, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: base_fn = _ORIGINAL_ATTENTION_FUNCTIONS.get("flash_attention_2") if not isinstance(module, IsaacVisionAttention) or base_fn is None: if base_fn is None: raise ValueError("Base flash attention function unavailable for fallback.") return base_fn( module, query, key, value, attention_mask, dropout=dropout, scaling=scaling, is_causal=is_causal, **kwargs, ) if query.dim() != 4 or query.size(0) != 1: raise ValueError("IsaacVisionAttention expects packed sequences with batch size 1 when using packed attention.") _, num_heads, seq_len, head_dim = query.shape q_lhd = query.transpose(1, 2).reshape(seq_len, num_heads, head_dim) k_lhd = key.transpose(1, 2).reshape(seq_len, num_heads, head_dim) v_lhd = value.transpose(1, 2).reshape(seq_len, num_heads, head_dim) cum_seq_q = kwargs.get("cu_seq_lens_q") cum_seq_k = kwargs.get("cu_seq_lens_k", cum_seq_q) max_seqlen = kwargs.get("max_length_q") effective_dropout = dropout if dropout is not None else (module.dropout if module.training else 0.0) effective_scaling = module.scale if scaling is None else scaling attn_mask = attention_mask if attn_mask is None: attn_mask = build_document_attention_mask( cu_seqlens=cum_seq_q, total_tokens=seq_len, dtype=q_lhd.dtype, device=q_lhd.device, ) attn_output_lhd, attn_weights = flash_attention_document_mask_forward( module, q_lhd, k_lhd, v_lhd, attention_mask=attn_mask, dropout=effective_dropout, scaling=effective_scaling, cum_seq_q=cum_seq_q, cum_seq_k=cum_seq_k, max_seqlen=max_seqlen, is_causal=is_causal, ) attn_output = attn_output_lhd.permute(1, 0, 2).unsqueeze(0) return attn_output, attn_weights def _isaac_sdpa_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], dropout: float = 0.0, scaling: Optional[float] = None, is_causal: bool = False, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: base_fn = _ORIGINAL_ATTENTION_FUNCTIONS.get("sdpa") if not isinstance(module, IsaacVisionAttention) or base_fn is None: if base_fn is None: raise ValueError("Base SDPA function unavailable for fallback.") return base_fn( module, query, key, value, attention_mask, dropout=dropout, scaling=scaling, is_causal=is_causal, **kwargs, ) if query.dim() != 4 or query.size(0) != 1: raise ValueError("IsaacVisionAttention expects packed sequences with batch size 1 when using packed attention.") _, num_heads, seq_len, head_dim = query.shape q_lhd = query.transpose(1, 2).reshape(seq_len, num_heads, head_dim) k_lhd = key.transpose(1, 2).reshape(seq_len, num_heads, head_dim) v_lhd = value.transpose(1, 2).reshape(seq_len, num_heads, head_dim) cum_seq = kwargs.get("cu_seq_lens_q") effective_dropout = dropout if dropout is not None else (module.dropout if module.training else 0.0) effective_scaling = module.scale if scaling is None else scaling attn_mask = attention_mask if attn_mask is None: attn_mask = build_document_attention_mask( cu_seqlens=cum_seq, total_tokens=seq_len, dtype=q_lhd.dtype, device=q_lhd.device, ) attn_output_lhd = sdpa_document_mask_forward( q_lhd, k_lhd, v_lhd, dropout=effective_dropout, scaling=effective_scaling, attention_mask=attn_mask, cu_seqlens=cum_seq, ) attn_output = attn_output_lhd.permute(1, 0, 2).unsqueeze(0) return attn_output, None def _isaac_eager_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], dropout: float = 0.0, scaling: Optional[float] = None, is_causal: bool = False, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: base_fn = _ORIGINAL_ATTENTION_FUNCTIONS.get("eager") if not isinstance(module, IsaacVisionAttention) or base_fn is None: if base_fn is None: raise ValueError("Base eager attention function unavailable for fallback.") return base_fn( module, query, key, value, attention_mask, dropout=dropout, scaling=scaling, is_causal=is_causal, **kwargs, ) if query.dim() != 4 or query.size(0) != 1: raise ValueError("IsaacVisionAttention expects packed sequences with batch size 1 when using packed attention.") _, num_heads, seq_len, head_dim = query.shape q_lhd = query.transpose(1, 2).reshape(seq_len, num_heads, head_dim) k_lhd = key.transpose(1, 2).reshape(seq_len, num_heads, head_dim) v_lhd = value.transpose(1, 2).reshape(seq_len, num_heads, head_dim) effective_scaling = module.scale if scaling is None else scaling attn_weights = torch.matmul(q_lhd, k_lhd.transpose(1, 2)) * effective_scaling if attention_mask is not None: mask = attention_mask if mask.dim() == 4: mask = mask.squeeze(0).squeeze(0) attn_weights = attn_weights + mask attn_weights = torch.softmax(attn_weights, dim=-1) if dropout and module.training: attn_weights = F.dropout(attn_weights, p=dropout, training=True) attn_output_lhd = torch.matmul(attn_weights, v_lhd) attn_output = attn_output_lhd.permute(1, 0, 2).unsqueeze(0) return attn_output, attn_weights ALL_ATTENTION_FUNCTIONS.register("isaac_flash_attention_2", _isaac_flash_attention_forward) ALL_ATTENTION_FUNCTIONS.register("isaac_flash_attention_3", _isaac_flash_attention_forward) ALL_ATTENTION_FUNCTIONS.register("isaac_sdpa", _isaac_sdpa_forward) ALL_ATTENTION_FUNCTIONS.register("isaac_eager", _isaac_eager_forward) def create_pixel_shuffle_index_map( seq_sizes: torch.Tensor, token_grids: torch.Tensor, scale_factor: int = 1, device: torch.device | None = None, ) -> torch.Tensor: """ Build a gather-index map that tells us, for every *output* token after pixel-shuffle, which `scale_factor**2` *input* tokens are being merged. Args ---- seq_sizes : (num_images,) - #patches in each image (row-major order) token_grids : (num_images,2) - (height, width) for every image scale_factor : spatial down-scale factor (≥2) device : (optional) overrides `seq_sizes.device` Returns ------- gather_idx : (new_total_seq_len, scale_factor**2) int64 tensor. gather_idx[i, j] is the *flat* index into the *original* packed sequence for the j-th sub-patch that forms the i-th output token. """ if device is None: device = seq_sizes.device scale_factor = int(scale_factor) if scale_factor < 2: raise ValueError("`scale_factor` must be ≥ 2") # Safety: all spatial dims must be divisible by the scale factor # Cannot run under torch compile fullgraph mode hence if not is_torchdynamo_compiling(): if not ( (token_grids[:, 0] % scale_factor == 0).all() and (token_grids[:, 1] % scale_factor == 0).all() ): raise AssertionError( "Every (H,W) in `token_grids` must be divisible by " f"scale_factor={scale_factor}, got {token_grids.tolist()}" ) gather_chunks: list[torch.Tensor] = [] tok_offset = 0 for seq_len, (h, w) in zip(seq_sizes.tolist(), token_grids.tolist(), strict=False): # Build the (H, W) grid of flat indices for this image grid = torch.arange(seq_len, device=device, dtype=torch.int64) + tok_offset grid = grid.view(h, w) # (H, W) # -------- identical ordering to your fixed-res routine -------- # Step 1: split width into blocks of scale_factor grid = grid.view(h, w // scale_factor, scale_factor) # (H, W/scale_factor, scale_factor) # Step 2: now split height into blocks of scale_factor grid = grid.view(h // scale_factor, scale_factor, w // scale_factor, scale_factor) # (H/scale_factor, scale_factor, W/scale_factor, scale_factor) # Step 3: final permutation to (H/scale_factor, W/scale_factor, scale_factor, scale_factor) grid = grid.permute(0, 2, 1, 3).contiguous() # (H/scale_factor, W/scale_factor, scale_factor, scale_factor) # Step 4: each (scale_factor, scale_factor) block forms one output token gather_chunks.append(grid.reshape(-1, scale_factor * scale_factor)) # (H*W / scale_factor**2, scale_factor**2) tok_offset += seq_len # Concatenate over all images in the packed batch gather_idx = torch.cat(gather_chunks, dim=0) # (Σ_i HᵢWᵢ/scale_factor**2, scale_factor**2) return gather_idx def pixel_shuffle_varlen( x: torch.Tensor, token_grids: torch.Tensor, scale_factor: int = 1, ) -> torch.Tensor: r"""Apply pixel shuffle to a packed vision sequence without unpacking per image. Args: x (`torch.Tensor`): Concatenated vision embeddings. Accepts `(seq_len, hidden_size)` or `(1, seq_len, hidden_size)` shapes produced by stacking image patches. token_grids (`torch.Tensor`): Integer tensor of shape `(num_images, 2)` whose rows give the `(height, width)` patch grid sizes corresponding to each image segment inside `x`. scale_factor (`int`, *optional*, defaults to 1): Spatial down-sampling factor specific to pixel shuffle. Values greater than one merge `scale_factor**2` neighboring patches into a single embedding channel-group. Returns: `torch.Tensor`: Pixel-shuffled embeddings with shape matching the input convention: `(seq_len, hidden_size * scale_factor**2)` when the input was 2D, or `(1, seq_len, hidden_size * scale_factor**2)` if the singleton batch dimension was present. Raises: ValueError: If more than one batch item is provided. """ keep_batch_dim = x.dim() == 3 if keep_batch_dim: if x.size(0) != 1: raise AssertionError("Packed sequence is expected to have batch_size == 1") x_ = x.squeeze(0) # (seq, embed) else: x_ = x # (seq, embed) embed_dim = x_.size(-1) scale_factor = int(scale_factor) # Calculate seq_sizes from token_grids seq_sizes = torch.prod(token_grids, dim=-1) # Build index map and gather in one go gather_idx = create_pixel_shuffle_index_map( seq_sizes=seq_sizes, token_grids=token_grids, scale_factor=scale_factor, device=x_.device, ) # (new_seq, scale_factor**2) # Gather → (new_seq, scale_factor**2, embed_dim) gathered = x_[gather_idx] # fancy indexing keeps gradient # Merge the scale_factor**2 group dimension into channels to finish the shuffle out = gathered.reshape(gathered.size(0), embed_dim * scale_factor * scale_factor) # Restore batch dimension if needed if keep_batch_dim: out = out.unsqueeze(0) return out class IsaacVisionTransformer(nn.Module): def __init__(self, config: IsaacVisionConfig): super().__init__() self.config = config self.embeddings = IsaacVisionEmbeddings(config) self.encoder = IsaacVisionEncoder(config) self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pixel_shuffle_scale_factor = config.pixel_shuffle_scale_factor def forward(self, packed_seq_patches: tuple[torch.Tensor, torch.Tensor]): seq_patches, token_grids = packed_seq_patches seq_sizes = torch.prod(token_grids, dim=-1) # Get embeddings from packed sequence hidden_states = self.embeddings(seq_patches, token_grids) # Add a pseudo batch dimension for the encoder hidden_states = hidden_states.unsqueeze(0) # Generate cumulative sequence lengths for variable-length attention cu_seqlens = torch.zeros(seq_sizes.size(0) + 1, dtype=torch.int32, device=hidden_states.device) cu_seqlens[1:] = seq_sizes.cumsum(0) max_seqlen = int(seq_sizes.max().item()) if seq_sizes.numel() > 0 else 0 # Pass through encoder with variable-length attention parameters encoder_outputs = self.encoder( inputs_embeds=hidden_states, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, return_dict=True, ) hidden_states = encoder_outputs.last_hidden_state # Apply final layer normalization hidden_states = self.post_layernorm(hidden_states) if self.pixel_shuffle_scale_factor > 1: hidden_states = pixel_shuffle_varlen( x=hidden_states, token_grids=token_grids, scale_factor=self.pixel_shuffle_scale_factor, ) # Remove the pseudo batch dimension we added earlier hidden_states = hidden_states.squeeze(0) # Return the full sequence of embeddings return hidden_states def get_scaled_image_size( scale: float, original_size: int, patch_size: int, pixel_shuffle_scale: int, ) -> int: scaled_size = scale * original_size divisor = patch_size * pixel_shuffle_scale scaled_size = math.ceil(scaled_size / divisor) * divisor scaled_size = max(divisor, scaled_size) return int(scaled_size) def get_image_size_for_max_num_patches( image_height: int, image_width: int, patch_size: int, max_num_patches: int, min_num_patches: int | None = None, eps: float = 1e-5, pixel_shuffle_scale: int = 1, ) -> tuple[int, int]: r"""Compute a target resolution whose patch grid satisfies patching parametrization. Args: image_height (`int`): Height in pixels of the source image prior to any resizing. image_width (`int`): Width in pixels of the source image prior to any resizing. patch_size (`int`): Size of the square patch used by the vision encoder. max_num_patches (`int`): Upper bound on `(height / patch_size) * (width / patch_size)` after resizing. min_num_patches (`int`, *optional*): Lower bound on the number of patches. When provided the image will be scaled up if necessary. eps (`float`, *optional*, defaults to 1e-5): Convergence tolerance for the internal binary search to determing the target dimensions. pixel_shuffle_scale (`int`, *optional*, defaults to 1): Additional stride multiplier applied when pixel shuffle later reduces spatial resolution. Returns: `tuple[int, int]`: Height and width (in pixels) that are multiples of `patch_size * pixel_shuffle_scale` and respect both the maximum and optional minimum patch-count constraints. """ # Ensure divisibility divisor = patch_size * pixel_shuffle_scale adjusted_height = math.ceil(image_height / divisor) * divisor adjusted_height = max(divisor, adjusted_height) adjusted_width = math.ceil(image_width / divisor) * divisor adjusted_width = max(divisor, adjusted_width) num_patches = (adjusted_height / patch_size) * (adjusted_width / patch_size) if min_num_patches is not None and num_patches < min_num_patches: # Scale up scale_min, scale_max = 1.0, 100.0 while (scale_max - scale_min) >= eps: scale = (scale_min + scale_max) / 2 target_height = get_scaled_image_size(scale, image_height, patch_size, pixel_shuffle_scale) target_width = get_scaled_image_size(scale, image_width, patch_size, pixel_shuffle_scale) num_patches = (target_height / patch_size) * (target_width / patch_size) if num_patches >= min_num_patches: scale_max = scale else: scale_min = scale scale = scale_max target_height = get_scaled_image_size(scale, image_height, patch_size, pixel_shuffle_scale) target_width = get_scaled_image_size(scale, image_width, patch_size, pixel_shuffle_scale) return target_height, target_width elif num_patches <= max_num_patches: return adjusted_height, adjusted_width else: # Scale down scale_min, scale_max = eps / 10, 1.0 while (scale_max - scale_min) >= eps: scale = (scale_min + scale_max) / 2 target_height = get_scaled_image_size(scale, image_height, patch_size, pixel_shuffle_scale) target_width = get_scaled_image_size(scale, image_width, patch_size, pixel_shuffle_scale) num_patches = (target_height / patch_size) * (target_width / patch_size) if num_patches <= max_num_patches: scale_min = scale else: scale_max = scale scale = scale_min target_height = get_scaled_image_size(scale, image_height, patch_size, pixel_shuffle_scale) target_width = get_scaled_image_size(scale, image_width, patch_size, pixel_shuffle_scale) return target_height, target_width def patchify_vision(image: torch.Tensor, patch_size: int) -> torch.Tensor: r"""Convert normalized images into flattened ViT-style patches. Args: image (`torch.Tensor`): Tensor of shape `(num_images, height, width, channels)`. patch_size (`int`): Edge length of the square patches Returns: `torch.Tensor`: Patch tensor where each position stores the flattened pixels belonging to that patch. Raises: ValueError: If `height` or `width` is not divisible by `patch_size`. """ num_images, height, width, channels = image.shape if height % patch_size or width % patch_size: raise ValueError(f"Dimensions of images {image.shape} are not divisible by patch_size={patch_size}.") patches = image.reshape(num_images, height // patch_size, patch_size, width // patch_size, patch_size, channels) patches = patches.permute(0, 1, 3, 2, 4, 5) patches = patches.reshape(num_images, height // patch_size, width // patch_size, channels * patch_size * patch_size) return patches class IsaacConfig(Qwen3Config): """Configuration class for Isaac multimodal model.""" model_type = "isaac" sub_configs = {"vision_config": IsaacVisionConfig, "text_config": Qwen3Config} image_processor_type = "IsaacImageProcessor" def __init__( self, vision_config: IsaacVisionConfig | None = None, text_config: Qwen3Config | dict | None = None, vision_rescale_factor: float = 1/255, max_sequence_length: int = 16384, vision_token: str = "", **kwargs, ): self._rope_scaling: dict[str, Any] | None = None resolved_text_config = kwargs.pop("text_config", text_config) if isinstance(resolved_text_config, Qwen3Config): text_config_kwargs = copy.deepcopy(resolved_text_config.to_dict()) elif isinstance(resolved_text_config, dict): text_config_kwargs = copy.deepcopy(resolved_text_config) elif resolved_text_config is None: text_config_kwargs = {} else: raise TypeError("`text_config` must be a mapping or `Qwen3Config` instance when provided.") text_config_kwargs.update(kwargs) super().__init__(**text_config_kwargs) self.text_config = Qwen3Config(**text_config_kwargs) if self._rope_scaling is None: self._rope_scaling = getattr(self.text_config, "rope_scaling", None) else: self.text_config.rope_scaling = self._rope_scaling # Handle vision config - either dict or IsaacVisionConfig instance if isinstance(vision_config, dict): self.vision_config = self.sub_configs["vision_config"](**vision_config) elif isinstance(vision_config, IsaacVisionConfig): self.vision_config = vision_config elif vision_config is None: self.vision_config = self.sub_configs["vision_config"]() # Vision normalization parameters self.vision_rescale_factor = float(vision_rescale_factor) # Processing parameters self.max_sequence_length = max_sequence_length self.vision_token = vision_token def get_text_config(self, *_, **kwargs) -> Qwen3Config: # Accept optional decoder/encoder flags to align with HF composite configs kwargs.pop("decoder", None) kwargs.pop("encoder", None) return self.text_config @property def rope_scaling(self): if hasattr(self, "text_config") and self.text_config is not None: return getattr(self.text_config, "rope_scaling", None) return self._rope_scaling @rope_scaling.setter def rope_scaling(self, value): self._rope_scaling = value if hasattr(self, "text_config") and self.text_config is not None: self.text_config.rope_scaling = value @property def vision_attn_implementation(self) -> str | None: value = getattr(self.vision_config, "_attn_implementation", None) if value is None: value = getattr(self.vision_config, "attn_implementation", None) return value @vision_attn_implementation.setter def vision_attn_implementation(self, value: str | None) -> None: self.vision_config._attn_implementation = value if value is not None: self.vision_config.attn_implementation = value elif hasattr(self.vision_config, "attn_implementation"): delattr(self.vision_config, "attn_implementation") # ============================================================================ # Processor Components # ============================================================================ def create_text_event(tokenizer: AutoTokenizer, text: str, time: float = 0.0) -> Event: r"""Wrap a text into an `Event` compatible with the multimodal TensorStream. Args: tokenizer (`AutoTokenizer`): Tokenizer used to convert text into model vocabulary ids. text (`str`): Plain-text fragment to encode. time (`float`, *optional*, defaults to 0.0): Timeline coordinate associated with the event. Both start and end times use the same value because text segments are instantaneous in the scheduler. Returns: `Event`: Event carrying a `(num_tokens, 1)` tensor of token ids with matching metadata so that downstream processors can compute modality-specific embeddings. """ tokens = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").squeeze(0) # Calculate dimensions for the event num_tokens = len(tokens) dims_virtual = [num_tokens, 1] # [sequence_length, 1] dims_real = dims_virtual.copy() # Ensure tokens has the right shape for tensor_stream_token_view # It expects a 2D tensor where sum(dim=-1) gives the token IDs if tokens.dim() == 1: tokens = tokens.unsqueeze(-1) return Event( data=tokens, type=TextType.text, time=(time, time), dims_virtual=dims_virtual, dims_real=dims_real, idx_range=(0, num_tokens), ) # ============================================================================ # Processor # ============================================================================ class IsaacProcessor(ProcessorMixin): attributes = ["image_processor", "tokenizer"] image_processor_class = ("IsaacImageProcessorFast",) tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast") valid_processor_kwargs = IsaacProcessorKwargs def __init__( self, image_processor: IsaacImageProcessorFast | None = None, tokenizer: Qwen2Tokenizer | None = None, *, vision_token: str = "", max_sequence_length: int = 16384, rescale_factor: float | None = None, config: IsaacConfig | dict | None = None, ) -> None: if tokenizer is None: raise ValueError("`tokenizer` must be provided to initialize IsaacProcessor.") if isinstance(config, dict): config = IsaacConfig(**config) if config is not None: max_sequence_length = config.max_sequence_length vision_token = config.vision_token rescale_factor = config.vision_rescale_factor resolved_rescale_factor = ( float(rescale_factor) if rescale_factor is not None else float(1/255) ) if config is not None: config.vision_rescale_factor = resolved_rescale_factor self.image_processor = image_processor super().__init__(image_processor, tokenizer) self.current_processor = self.image_processor self.config = config # Mirror tokenizer chat template so ProcessorMixin.apply_chat_template works. self.chat_template = getattr(self.tokenizer, "chat_template", None) self.vision_token = vision_token self.max_sequence_length = max_sequence_length def build_event_stream_simple( self, text: str, images: list[PIL.Image.Image] | None = None, ) -> Stream: events = [] # Process text and images # Find all occurrences of vision token pattern = re.escape(self.vision_token) parts = re.split(f"({pattern})", text) # Keep the delimiter in the result image_idx = 0 for current_time, part in enumerate(parts): if part == self.vision_token: # Replace vision token with image event if images is None or image_idx >= len(images): raise ValueError("Encountered vision token without a corresponding image.") features = self.image_processor( images=images[image_idx], return_tensors=TensorType.PYTORCH, ) patches = features["patches"][0] # (H_tokens, W_tokens, embed) virtual_dims = features["virtual_pixel_size"][0].tolist() real_dims = features["real_pixel_size"][0].tolist() vision_event = Event( data=patches.reshape(-1, patches.shape[-1]), type=VisionType.image, time=(current_time, current_time), dims_virtual=virtual_dims, dims_real=real_dims, idx_range=(0, math.prod(virtual_dims)), ) events.append(vision_event) image_idx += 1 elif part: # Non-empty text part # tokens = self.text_processor.tokenize(part, add_special_tokens=False) text_event = create_text_event(self.tokenizer, part, time=current_time) events.append(text_event) # Create stream without scheduling (events already in order) return create_stream(events, priority=[TextType.text, VisionType.image], schedule=True) def __call__( self, text: str | list[str], images: PIL.Image.Image | list[PIL.Image.Image] | None = None, return_tensors: str | TensorType | None = TensorType.PYTORCH, **kwargs, ) -> BatchFeature: """ Process text and images into TensorStream format. Args: text: Input text or list of texts with vision tokens images: PIL image or list of images (optional) return_tensors: Format for output tensors Returns: BatchFeature with input_ids and tensor_stream """ # Normalize inputs to lists if isinstance(text, str): texts = [text] else: texts = text if images is not None: if isinstance(images, PIL.Image.Image): images_list = [images] else: images_list = images else: images_list = None if len(texts) != 1: raise ValueError("IsaacProcessor currently supports batch_size=1") if images_list is not None: # Count vision tokens in text to validate image count vision_token_count = texts[0].count(self.vision_token) if vision_token_count != len(images_list): raise ValueError( f"Number of {self.vision_token} tokens in text ({vision_token_count}) " f"must match number of images ({len(images_list)})" ) # Build event stream stream = self.build_event_stream_simple( text=texts[0], images=images_list, ) # Create TensorStream tensor_stream = TensorStream([stream]) # Slice to max length if needed _, T = tensor_stream.shape if T > self.max_sequence_length: tensor_stream = ts_slice(tensor_stream, start=T - self.max_sequence_length, end=T) # Get token view tokens = tensor_stream_token_view(tensor_stream) if return_tensors in (TensorType.PYTORCH, "pt"): input_ids = torch.as_tensor(tokens, dtype=torch.long) else: input_ids = tokens data = { "input_ids": input_ids, "tensor_stream": tensor_stream, } return BatchFeature(data=data) # ============================================================================ # Model # ============================================================================ def compute_position_ids_input_ids(input_ids: torch.Tensor) -> torch.Tensor: r"""Create 3D positional indices for token input. Args: input_ids (`torch.Tensor`): Tensor of shape `(batch_size, seq_len)` containing token ids. Returns: `torch.Tensor`: Positional indices with shape `(batch_size, seq_len, 3)` where each channel duplicates the 1D position so it can be consumed by the 3-axis MRoPE rotary embedding. """ batch_size, seq_length = input_ids.shape position_ids = torch.arange(seq_length, device=input_ids.device) position_ids = position_ids.view(1, -1).expand(batch_size, -1) position_ids = position_ids.unsqueeze(2).expand(-1, -1, 3) # Add 3D for MRoPE return position_ids class IsaacRotaryEmbedding(nn.Module): EXTRA_ROPE_KEYS = {"mrope_section", "mrope_interleaved"} def __init__(self, config: IsaacConfig, device=None): super().__init__() rope_source_cfg = config.get_text_config() if hasattr(config, "get_text_config") else config rope_scaling = getattr(rope_source_cfg, "rope_scaling", None) or {} sanitized_scaling = {k: v for k, v in rope_scaling.items() if k not in self.EXTRA_ROPE_KEYS} config_for_rope = copy.copy(rope_source_cfg) config_for_rope.rope_scaling = sanitized_scaling if sanitized_scaling else None init_device = device if device is not None and getattr(device, "type", None) != "meta" else None self._qwen_rotary = Qwen2_5_VLRotaryEmbedding(config_for_rope, device=init_device) rotary_half_dim = self._qwen_rotary.inv_freq.shape[0] self.mrope_section = self._resolve_mrope_section(rope_scaling.get("mrope_section"), rotary_half_dim) self.hidden_size = getattr(rope_source_cfg, "hidden_size", None) or config.hidden_size @staticmethod def _resolve_mrope_section(section: list[int] | None, rotary_half_dim: int) -> list[int]: if section is None: weights = (2, 1, 1) base = [rotary_half_dim * w // sum(weights) for w in weights] base[0] += rotary_half_dim - sum(base) return base section = [int(v) for v in section] if len(section) != 3: raise ValueError("`mrope_section` must contain exactly three elements (temporal, height, width)") if sum(section) != rotary_half_dim: raise ValueError( f"`mrope_section` must sum to the rotary half-dimension ({rotary_half_dim}). Received {section}." ) return section def _combine_axes(self, tensor: torch.Tensor) -> torch.Tensor: split_sections = tuple(self.mrope_section * 2) chunks = tensor.split(split_sections, dim=-1) return torch.cat([chunk[i % 3] for i, chunk in enumerate(chunks)], dim=-1) @property def inv_freq(self) -> torch.Tensor: return self._qwen_rotary.inv_freq def forward( self, position_ids: torch.Tensor, modality_tensor: torch.Tensor, hidden_states: torch.Tensor | None = None, ) -> tuple[torch.Tensor, torch.Tensor]: if position_ids.ndim != 3 or position_ids.size(-1) != 3: raise ValueError("`position_ids` must have shape (batch, seq_len, 3) for MRoPE") if modality_tensor.shape != position_ids.shape[:2]: raise ValueError("`modality_tensor` must align with the first two dims of `position_ids`") if hidden_states is None: batch, seq_len, _ = position_ids.shape hidden_states = torch.zeros( batch, seq_len, self.hidden_size, dtype=torch.float32, device=position_ids.device, ) with torch.no_grad(): pos = position_ids.clone() not_spatial = modality_tensor != VisionType.image.value if not_spatial.any(): data_1d = pos[not_spatial][..., 0].unsqueeze(-1) pos[not_spatial] = data_1d.expand(-1, pos.shape[-1]) pos_axes = pos.permute(2, 0, 1).contiguous() cos_axes, sin_axes = self._qwen_rotary(hidden_states, pos_axes) cos_axes = cos_axes.to(hidden_states.dtype) sin_axes = sin_axes.to(hidden_states.dtype) cos_combined = self._combine_axes(cos_axes) sin_combined = self._combine_axes(sin_axes) return cos_combined, sin_combined class IsaacModel(Qwen3PreTrainedModel): supports_gradient_checkpointing = True def __init__(self, config: IsaacConfig): Qwen3PreTrainedModel.__init__(self, config) text_cfg_source = getattr(config, "get_text_config", lambda: config)() text_cfg = copy.deepcopy(text_cfg_source) text_cfg._attn_implementation = config._attn_implementation self.text_model = AutoModel.from_config(text_cfg) # Ensure downstream callers observe the composed config self.text_model.config = config self.rotary_emb = IsaacRotaryEmbedding(config, device=self.device) if config.vision_config is None: raise ValueError("IsaacConfig should always have vision_config") hidden_dim = config.vision_config.hidden_size * (config.vision_config.pixel_shuffle_scale_factor**2) self.vision_embedding = nn.Sequential( IsaacVisionTransformer(config.vision_config), nn.Linear( hidden_dim, 4 * hidden_dim, bias=False, ), nn.SiLU(), nn.Linear(4 * hidden_dim, config.hidden_size, bias=False), ) # Dispatch table for TensorStream balanced embedding (text + vision) self.embed_fns = { TextType: self.embed_text_tokens, VisionType: self.embed_vision, } def get_input_embeddings(self) -> nn.Module: return self.text_model.get_input_embeddings() def set_input_embeddings(self, value: nn.Module) -> None: self.text_model.set_input_embeddings(value) @property def embed_tokens(self) -> nn.Module: return self.text_model.embed_tokens @embed_tokens.setter def embed_tokens(self, value: nn.Module) -> None: self.text_model.embed_tokens = value @property def layers(self) -> nn.ModuleList: return self.text_model.layers @property def norm(self) -> nn.Module: return self.text_model.norm def _set_gradient_checkpointing(self, enable: bool = True, gradient_checkpointing_func=None): self.text_model._set_gradient_checkpointing( enable=enable, gradient_checkpointing_func=gradient_checkpointing_func ) def embed_text_tokens(self, token_ids: torch.Tensor) -> torch.Tensor: """Embed text tokens, squeezing singleton dimensions.""" # Text events are shaped as (..., 1); squeeze the singleton index dim h = self.text_model.embed_tokens(token_ids) if h.dim() >= 2 and h.size(-2) == 1: h = h[..., 0, :] return h def embed_vision(self, vision_tokens: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: """Embed vision tokens using the vision encoder.""" # vision tokens is (seq_patches, token_grids) return self.vision_embedding(vision_tokens) def embed_stream(self, tensor_stream: TensorStream) -> torch.Tensor: """ Embed each modality stream independently, preserving the original TensorStream structure. """ flat_stream = tensor_stream.flat_stream() per_modality_stream = group_streams(flat_stream, group_fn=lambda ev: ev.type, schedule=False) per_modality_compact_stream = {k: v.compact() for k, v in per_modality_stream.items()} # Collect per-event grids for vision tokens (H, W like dims sans time) token_grids = defaultdict(list) for stream in tensor_stream.streams: for event in stream: token_grids[event.type].append(event.dims(virtual=False)) embedded_compact = {} for stream_type, modality_payload_tensor in per_modality_compact_stream.items(): if stream_type.modality == VisionType: # Build a (N_events, 2) grid tensor with spatial dims only grids = token_grids.get(stream_type, []) if len(grids) == 0: input_tensor = modality_payload_tensor else: token_grids_tensor = torch.tensor(grids, dtype=torch.long, device=tensor_stream.device)[:, 1:] input_tensor = (modality_payload_tensor, token_grids_tensor) embedded_compact[stream_type] = self.embed_fns[stream_type.modality](input_tensor) else: embedded_compact[stream_type] = self.embed_fns[stream_type.modality](modality_payload_tensor) # Reconstruct a TensorStream with embedded payloads and compact embedded_ts = reconstruct_tensor_stream_from_compact_dict(tensor_stream, embedded_compact) h = embedded_ts.compact() # (B, T, D) return h def forward( self, input_ids: torch.LongTensor | None = None, tensor_stream: TensorStream | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, modality_tensor: torch.LongTensor | None = None, past_key_values: list[torch.FloatTensor] | None = None, inputs_embeds: torch.FloatTensor | None = None, use_cache: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, cache_position: torch.LongTensor | None = None, **kwargs, ) -> tuple | BaseModelOutputWithPast: """ Forward pass with MRoPE position embeddings. Computes position embeddings once and passes them through all layers. """ output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Get inputs if tensor_stream is not None and inputs_embeds is not None: raise ValueError("You cannot specify both tensor_stream and inputs_embeds") elif tensor_stream is not None: # Embed TensorStream directly inputs_embeds = self.embed_stream(tensor_stream) # Create modality tensor if not provided if modality_tensor is None: modality_tensor = modality_mask(tensor_stream) elif input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: inputs_embeds = self.text_model.embed_tokens(input_ids) # Create text modality tensor if not provided if modality_tensor is None: batch_size, seq_length = input_ids.shape modality_tensor = torch.full( (batch_size, seq_length), TextType.text.value, device=input_ids.device, dtype=torch.long ) elif inputs_embeds is None: raise ValueError("You have to specify either tensor_stream, input_ids or inputs_embeds") # Create default position_ids if not provided if position_ids is None: if tensor_stream is not None: position_ids = compute_mrope_pos_tensor(tensor_stream) # (B,L,3) else: position_ids = compute_position_ids_input_ids(input_ids) # Compute MRoPE position embeddings if we have custom rotary_emb cos, sin = self.rotary_emb( position_ids, modality_tensor, hidden_states=inputs_embeds, ) cos = cos.to(inputs_embeds.dtype) sin = sin.to(inputs_embeds.dtype) # Prepare attention mask if attention_mask is not None: attention_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, False ) # Initialize hidden states hidden_states = inputs_embeds for decoder_layer in self.text_model.layers: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, use_cache=use_cache, cache_position=cache_position, position_embeddings=(cos, sin), **kwargs, ) hidden_states = layer_outputs[0] if isinstance(layer_outputs, tuple) else layer_outputs # Final layer norm hidden_states = self.text_model.norm(hidden_states) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, ) def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and past_key_values is not None: is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0] if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of Qwen3. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if ( self.config._attn_implementation == "sdpa" and not (using_static_cache or using_sliding_window_cache) and not output_attentions ): if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, sliding_window=self.config.sliding_window, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] # SlidingWindowCache or StaticCache if using_sliding_window_cache or using_static_cache: target_length = past_key_values.get_max_cache_shape() # DynamicCache or no cache else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], config=self.config, past_key_values=past_key_values, ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, config: Qwen3Config, past_key_values: Cache, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to place the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. config (`Qwen3Config`): The model's configuration class past_key_values (`Cache`): The cache class that is being used currently to generate """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) if config.sliding_window is not None: # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also # the check is needed to verify is current checkpoint was trained with sliding window or not if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length: sliding_attend_mask = torch.arange(target_length, device=device) <= ( cache_position.reshape(-1, 1) - config.sliding_window ) diagonal_attend_mask.bitwise_or_(sliding_attend_mask) causal_mask *= diagonal_attend_mask causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit if attention_mask.shape[-1] > target_length: attention_mask = attention_mask[:, :target_length] mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask class IsaacForConditionalGeneration(Qwen3ForCausalLM, GenerationMixin): """Isaac multimodal model for conditional generation.""" config_class = IsaacConfig def __init__(self, config: IsaacConfig): super().__init__(config) self.model = IsaacModel(config) # Use our custom model self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Tracks rotary position offsets computed during a full forward pass so decode steps can reuse them. self.rope_deltas = None def get_rope_index( self, input_ids: torch.Tensor | None, tensor_stream: TensorStream | None, attention_mask: torch.Tensor | None, ) -> tuple[torch.Tensor, torch.Tensor]: """Compute MRoPE position ids from a TensorStream (or 1D fallback). Returns (position_ids, rope_deltas). position_ids is (B,L,3) for MRoPE. rope_deltas is (B,1) used to advance positions in decode. """ # tensor_stream present: compute 3D coords if tensor_stream is None and input_ids is None: raise ValueError("`tensor_stream` or `input_ids` must be provided to compute rope indices") if tensor_stream is not None: pos_3d = compute_mrope_pos_tensor(tensor_stream) # (B,L,3) else: pos_3d = compute_position_ids_input_ids(input_ids) B, L, _ = pos_3d.shape # Max position per batch across the 3 planes and sequence dimension: (B,) m_per_batch = pos_3d.amax(dim=(1, 2)) # Sequence lengths per batch: (B,) if attention_mask is None: seq_lens = torch.full_like(m_per_batch, L) else: seq_lens = attention_mask.eq(1).sum(dim=-1).to(dtype=m_per_batch.dtype, device=m_per_batch.device) rope_deltas = (m_per_batch + 1 - seq_lens).to(dtype=pos_3d.dtype).unsqueeze(1) return pos_3d, rope_deltas def forward( self, input_ids: torch.LongTensor | None = None, tensor_stream: TensorStream | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: list[torch.FloatTensor] | None = None, inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, cache_position: torch.LongTensor | None = None, **kwargs, ) -> tuple | CausalLMOutputWithPast: """ Forward pass for conditional generation supporting both standard inputs and TensorStream. Uses our embed_stream approach for multimodal inputs. """ # Don't compute embeddings here - let the model handle it if tensor_stream is not None: input_ids = None if input_ids is None and inputs_embeds is None and tensor_stream is None: raise ValueError("Either input_ids, inputs_embeds, or tensor_stream must be provided.") # Build position ids (MRoPE) if needed and tensor_stream is available # During decode we reuse `self.rope_deltas` computed on the initial forward pass; `rope_delta` captures how far # cached rotary phases have progressed so we can advance `position_ids` without rebuilding the TensorStream. if position_ids is None and tensor_stream is not None: position_ids, self.rope_deltas = self.get_rope_index(input_ids, tensor_stream, attention_mask) elif position_ids is None and input_ids is not None: # For text inputs build position ids and modality tensor position_ids = compute_position_ids_input_ids(input_ids) if cache_position is not None and self.rope_deltas is not None: # Combine the incremental decode step (`cache_position`) with cached offsets so hidden states continue # rotating in lockstep across generation steps. rope_delta = (cache_position[0] + self.rope_deltas).to(input_ids.device) else: rope_delta = 0 if cache_position is not None and not isinstance(rope_delta, int): # otherwise `deltas` is an int `0` batch_size = input_ids.shape[0] rope_delta = rope_delta.repeat_interleave(batch_size // rope_delta.shape[0], dim=0) position_ids = position_ids.add(rope_delta) if tensor_stream is not None: modality_tensor = modality_mask(tensor_stream) else: batch_size, seq_len = input_ids.shape modality_tensor = torch.empty(batch_size, seq_len, device=position_ids.device).fill_(TextType.text.value) outputs = self.model( input_ids=input_ids, tensor_stream=tensor_stream, attention_mask=attention_mask, position_ids=position_ids, modality_tensor=modality_tensor, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, **kwargs, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=None, ) def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, past_key_values: list[torch.FloatTensor] | None = None, attention_mask: torch.Tensor | None = None, inputs_embeds: torch.FloatTensor | None = None, tensor_stream: TensorStream | None = None, cache_position: torch.LongTensor | None = None, position_ids: torch.LongTensor | None = None, use_cache: bool = True, **kwargs, ) -> dict[str, Any]: """ Prepare inputs for generation, handling TensorStream inputs properly. """ # Call parent preparation model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, cache_position=cache_position, position_ids=position_ids, use_cache=use_cache, **kwargs, ) # Handle TensorStream for first forward pass only if tensor_stream is not None and (cache_position is None or cache_position[0] == 0): model_inputs["tensor_stream"] = tensor_stream # Let forward rebuild position_ids using cached deltas during decode model_inputs["position_ids"] = None # Drop tensor_stream after step 0 if cache_position is not None and cache_position[0] != 0: model_inputs["tensor_stream"] = None return model_inputs def can_generate(self) -> bool: return True AutoImageProcessor.register( IsaacConfig, fast_image_processor_class=IsaacImageProcessorFast, exist_ok=True, ) __all__ = [ "IsaacConfig", "IsaacModel", "IsaacForConditionalGeneration", "IsaacImageProcessorFast", "IsaacProcessor", ] def _compute_residual_p_frames(frames: torch.Tensor, is_p_frame: list[bool]) -> torch.Tensor: """Compute residuals for P-frames to stay in sync with the training pipeline.""" if not any(is_p_frame): return frames frame_indices = torch.arange(len(is_p_frame), device=frames.device) i_frame_mask = torch.tensor([not flag for flag in is_p_frame], device=frames.device) last_i_indices = torch.cummax((i_frame_mask * (1 + frame_indices)), dim=0).values.long() - 1 p_indices = frame_indices[torch.tensor(is_p_frame, device=frames.device)] frames[p_indices] = frames[p_indices] - frames[last_i_indices[p_indices]] return frames