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config.json

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+{
+  "architectures": [
+    "SMBVisionModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_smb_vision.SMBVisionModelConfig",
+    "AutoModel": "modeling_smb_vision.SMBVisionModel"
+  },
+  "dtype": "bfloat16",
+  "hidden_size": 1152,
+  "masking_ratio": 0.65,
+  "model_type": "smb_vision_model",
+  "predictor_config": {
+    "depth": 12,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 512,
+    "in_channels": 1,
+    "in_hidden_size": 1152,
+    "initializer_range": 0.02,
+    "intermediate_size": 1536,
+    "model_type": "smb_vision_predictor",
+    "num_heads": 16
+  },
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "vision_config": {
+    "deepstack_visual_indexes": [
+      8,
+      16,
+      24
+    ],
+    "depth": 27,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 1,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "smb_vision_encoder",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 2048,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 16
+  }
+}

configuration_smb_vision.py

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+# coding=utf-8
+# Copyright 2025 The SMB Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from transformers.configuration_utils import PretrainedConfig
+
+
+class SMBVisionConfig(PretrainedConfig):
+    model_type = "smb_vision_encoder"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=27,
+        hidden_size=1152,
+        hidden_act="gelu_pytorch_tanh",
+        intermediate_size=4304,
+        num_heads=16,
+        in_channels=3,
+        patch_size=16,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        out_hidden_size=3584,
+        num_position_embeddings=2304,
+        deepstack_visual_indexes=[8, 16, 24],
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.out_hidden_size = out_hidden_size
+        self.num_position_embeddings = num_position_embeddings
+        self.initializer_range = initializer_range
+        self.deepstack_visual_indexes = deepstack_visual_indexes
+
+
+class SMBVisionPredictorConfig(PretrainedConfig):
+    model_type = "smb_vision_predictor"
+    base_config_key = "predictor_config"
+
+    def __init__(
+        self,
+        depth=27,
+        in_hidden_size=1152,
+        hidden_size=512,
+        hidden_act="gelu_pytorch_tanh",
+        intermediate_size=1536,
+        num_heads=16,
+        in_channels=1,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.in_hidden_size = in_hidden_size
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.initializer_range = initializer_range
+
+
+class SMBVisionModelConfig(PretrainedConfig):
+    model_type = "smb_vision_model"
+    sub_configs = {"vision_config": SMBVisionConfig, "predictor_config": SMBVisionPredictorConfig}
+
+    def __init__(
+        self,
+        vision_config=None,
+        predictor_config=None,
+        hidden_size=1152,
+        masking_ratio=0.1,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(predictor_config, dict):
+            self.predictor_config = self.sub_configs["predictor_config"](**predictor_config)
+        elif predictor_config is None:
+            self.predictor_config = self.sub_configs["predictor_config"]()
+
+        self.hidden_size = hidden_size
+        self.masking_ratio = masking_ratio
+
+        super().__init__(**kwargs)
+
+
+__all__ = ["SMBVisionConfig", "SMBVisionPredictorConfig", "SMBVisionModelConfig"]

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07c086b52183afd27fca41a807a7c3b9359913143a243f8299b1c279ef2d6922
+size 1224159656

modeling_smb_vision.py

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+# coding=utf-8
+# Copyright 2025 The SMB Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import BaseModelOutput, ModelOutput
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs
+from .configuration_smb_vision import (
+    SMBVisionConfig,
+    SMBVisionPredictorConfig,
+    SMBVisionModelConfig,
+)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
+        query.dtype
+    )
+    attn_weights = nn.functional.dropout(
+        attn_weights, p=dropout, training=module.training
+    )
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class SMBVisionMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.linear_fc1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
+        self.linear_fc2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=True)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_state):
+        return self.linear_fc2(self.act_fn(self.linear_fc1(hidden_state)))
+
+
+class SMBVisionPatchEmbed(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        self.patch_size = config.patch_size
+        self.temporal_patch_size = config.temporal_patch_size
+        self.in_channels = config.in_channels
+        self.embed_dim = config.hidden_size
+
+        kernel_size = [self.temporal_patch_size, self.patch_size, self.patch_size]
+        for in_channels in [1, 3, 4]:
+            setattr(
+                self,
+                f"proj_c{in_channels}",
+                nn.Conv3d(
+                    in_channels,
+                    self.embed_dim,
+                    kernel_size=kernel_size,
+                    stride=kernel_size,
+                    bias=True,
+                ),
+            )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        target_dtype = self.proj_c1.weight.dtype
+        if self.in_channels == 1:  # grayscale
+            hidden_states = hidden_states.view(
+                -1, 1, self.temporal_patch_size, self.patch_size, self.patch_size
+            )
+            hidden_states = self.proj_c1(hidden_states.to(dtype=target_dtype)).view(
+                -1, self.embed_dim
+            )
+        elif self.in_channels == 3:  # rgb
+            hidden_states = hidden_states.view(
+                -1, 3, self.temporal_patch_size, self.patch_size, self.patch_size
+            )
+            hidden_states = self.proj_c3(hidden_states.to(dtype=target_dtype)).view(
+                -1, self.embed_dim
+            )
+        elif self.in_channels == 4:  # multi sequence
+            hidden_states = hidden_states.view(
+                -1, 4, self.temporal_patch_size, self.patch_size, self.patch_size
+            )
+            hidden_states = self.proj_c4(hidden_states.to(dtype=target_dtype)).view(
+                -1, self.embed_dim
+            )
+        else:
+            raise ValueError(f"Unsupported number of channels: {self.in_channels}")
+        return hidden_states
+
+
+class SMBVisionRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        seq = torch.arange(
+            seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
+        )
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs
+
+
+class SMBVisionPatchMerger(nn.Module):
+    def __init__(self, config, use_postshuffle_norm=False) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size * (config.spatial_merge_size**2)
+        self.use_postshuffle_norm = use_postshuffle_norm
+        self.norm = nn.LayerNorm(
+            self.hidden_size if use_postshuffle_norm else config.hidden_size, eps=1e-6
+        )
+        self.linear_fc1 = nn.Linear(self.hidden_size, self.hidden_size)
+        self.act_fn = nn.GELU()
+        self.linear_fc2 = nn.Linear(self.hidden_size, config.out_hidden_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.norm(
+            x.contiguous().view(-1, self.hidden_size)
+            if self.use_postshuffle_norm
+            else x
+        ).view(-1, self.hidden_size)
+        x = self.linear_fc2(self.act_fn(self.linear_fc1(x)))
+        return x
+
+
+def apply_rotary_pos_emb_vision(
+    q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    orig_q_dtype = q.dtype
+    orig_k_dtype = k.dtype
+    q, k = q.float(), k.float()
+    cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    q_embed = q_embed.to(orig_q_dtype)
+    k_embed = k_embed.to(orig_k_dtype)
+    return q_embed, k_embed
+
+
+class SMBVisionAttention(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        self.dim = config.hidden_size
+        self.num_heads = config.num_heads
+        self.head_dim = self.dim // self.num_heads
+        self.num_key_value_groups = 1  # needed for eager attention
+        self.qkv = nn.Linear(self.dim, self.dim * 3, bias=True)
+        self.proj = nn.Linear(self.dim, self.dim)
+        self.scaling = self.head_dim**-0.5
+        self.config = config
+        self.attention_dropout = 0.0
+        self.is_causal = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        seq_length = hidden_states.shape[0]
+        query_states, key_states, value_states = (
+            self.qkv(hidden_states)
+            .reshape(seq_length, 3, self.num_heads, -1)
+            .permute(1, 0, 2, 3)
+            .unbind(0)
+        )
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb_vision(
+            query_states, key_states, cos, sin
+        )
+
+        query_states = query_states.transpose(0, 1).unsqueeze(0)
+        key_states = key_states.transpose(0, 1).unsqueeze(0)
+        value_states = value_states.transpose(0, 1).unsqueeze(0)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[
+                self.config._attn_implementation
+            ]
+
+        if self.config._attn_implementation == "flash_attention_2":
+            # Flash Attention 2: Use cu_seqlens for variable length attention
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+            attn_output, _ = attention_interface(
+                self,
+                query_states,
+                key_states,
+                value_states,
+                attention_mask=None,
+                scaling=self.scaling,
+                dropout=0.0 if not self.training else self.attention_dropout,
+                cu_seq_lens_q=cu_seqlens,
+                cu_seq_lens_k=cu_seqlens,
+                max_length_q=max_seqlen,
+                max_length_k=max_seqlen,
+                is_causal=False,
+                **kwargs,
+            )
+        else:
+            # Other implementations: Process each chunk separately
+            lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+            splits = [
+                torch.split(tensor, lengths.tolist(), dim=2)
+                for tensor in (query_states, key_states, value_states)
+            ]
+
+            attn_outputs = [
+                attention_interface(
+                    self,
+                    q,
+                    k,
+                    v,
+                    attention_mask=None,
+                    scaling=self.scaling,
+                    dropout=0.0 if not self.training else self.attention_dropout,
+                    is_causal=False,
+                    **kwargs,
+                )[0]
+                for q, k, v in zip(*splits)
+            ]
+            attn_output = torch.cat(attn_outputs, dim=1)
+
+        attn_output = attn_output.reshape(seq_length, -1).contiguous()
+        attn_output = self.proj(attn_output)
+        return attn_output
+
+
+class SMBVisionBlock(GradientCheckpointingLayer):
+    def __init__(self, config, attn_implementation: str = "sdpa") -> None:
+        super().__init__()
+        self.norm1 = nn.LayerNorm(config.hidden_size, eps=1e-6)
+        self.norm2 = nn.LayerNorm(config.hidden_size, eps=1e-6)
+        self.attn = SMBVisionAttention(config=config)
+        self.mlp = SMBVisionMLP(config=config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        hidden_states = hidden_states + self.attn(
+            self.norm1(hidden_states),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+        return hidden_states
+
+
+class SMBVisionEncoder(PreTrainedModel):
+    config: SMBVisionConfig
+    _no_split_modules = ["SMBVisionBlock"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _can_compile_fullgraph = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+    _supports_attention_backend = True
+
+    def __init__(self, config, *inputs, **kwargs) -> None:
+        super().__init__(config, *inputs, **kwargs)
+        self.spatial_merge_size = config.spatial_merge_size
+        self.patch_size = config.patch_size
+        self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
+
+        self.patch_embed = SMBVisionPatchEmbed(
+            config=config,
+        )
+
+        self.pos_embed = nn.Embedding(
+            config.num_position_embeddings, config.hidden_size
+        )
+        self.num_grid_per_side = int(config.num_position_embeddings**0.5)
+
+        head_dim = config.hidden_size // config.num_heads
+        self.rotary_pos_emb = SMBVisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList(
+            [SMBVisionBlock(config) for _ in range(config.depth)]
+        )
+        self.merger = SMBVisionPatchMerger(
+            config=config,
+            use_postshuffle_norm=False,
+        )
+
+        self.deepstack_visual_indexes = config.deepstack_visual_indexes
+        self.deepstack_merger_list = nn.ModuleList(
+            [
+                SMBVisionPatchMerger(
+                    config=config,
+                    use_postshuffle_norm=True,
+                )
+                for _ in range(len(config.deepstack_visual_indexes))
+            ]
+        )
+
+        self.gradient_checkpointing = False
+
+    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        merge_size = self.spatial_merge_size
+
+        max_hw = int(grid_thw[:, 1:].max().item())
+        freq_table = self.rotary_pos_emb(max_hw)  # (max_hw, dim // 2)
+        device = freq_table.device
+
+        total_tokens = int(torch.prod(grid_thw, dim=1).sum().item())
+        pos_ids = torch.empty((total_tokens, 2), dtype=torch.long, device=device)
+
+        offset = 0
+        for num_frames, height, width in grid_thw:
+            merged_h, merged_w = height // merge_size, width // merge_size
+
+            block_rows = torch.arange(merged_h, device=device)  # block row indices
+            block_cols = torch.arange(merged_w, device=device)  # block col indices
+            intra_row = torch.arange(
+                merge_size, device=device
+            )  # intra-block row offsets
+            intra_col = torch.arange(
+                merge_size, device=device
+            )  # intra-block col offsets
+
+            # Compute full-resolution positions
+            row_idx = (
+                block_rows[:, None, None, None] * merge_size
+                + intra_row[None, None, :, None]
+            )
+            col_idx = (
+                block_cols[None, :, None, None] * merge_size
+                + intra_col[None, None, None, :]
+            )
+
+            row_idx = row_idx.expand(
+                merged_h, merged_w, merge_size, merge_size
+            ).reshape(-1)
+            col_idx = col_idx.expand(
+                merged_h, merged_w, merge_size, merge_size
+            ).reshape(-1)
+
+            coords = torch.stack((row_idx, col_idx), dim=-1)
+
+            if num_frames > 1:
+                coords = coords.repeat(num_frames, 1)
+
+            num_tokens = coords.shape[0]
+            pos_ids[offset : offset + num_tokens] = coords
+            offset += num_tokens
+
+        embeddings = freq_table[pos_ids]  # lookup rotary embeddings
+        embeddings = embeddings.flatten(1)
+        return embeddings
+
+    def fast_pos_embed_interpolate(self, grid_thw):
+        grid_ts, grid_hs, grid_ws = grid_thw[:, 0], grid_thw[:, 1], grid_thw[:, 2]
+
+        idx_list = [[] for _ in range(4)]
+        weight_list = [[] for _ in range(4)]
+
+        for t, h, w in zip(grid_ts, grid_hs, grid_ws):
+            h_idxs = torch.linspace(0, self.num_grid_per_side - 1, h)
+            w_idxs = torch.linspace(0, self.num_grid_per_side - 1, w)
+
+            h_idxs_floor = h_idxs.int()
+            w_idxs_floor = w_idxs.int()
+            h_idxs_ceil = (h_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+            w_idxs_ceil = (w_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+
+            dh = h_idxs - h_idxs_floor
+            dw = w_idxs - w_idxs_floor
+
+            base_h = h_idxs_floor * self.num_grid_per_side
+            base_h_ceil = h_idxs_ceil * self.num_grid_per_side
+
+            indices = [
+                (base_h[None].T + w_idxs_floor[None]).flatten(),
+                (base_h[None].T + w_idxs_ceil[None]).flatten(),
+                (base_h_ceil[None].T + w_idxs_floor[None]).flatten(),
+                (base_h_ceil[None].T + w_idxs_ceil[None]).flatten(),
+            ]
+
+            weights = [
+                ((1 - dh)[None].T * (1 - dw)[None]).flatten(),
+                ((1 - dh)[None].T * dw[None]).flatten(),
+                (dh[None].T * (1 - dw)[None]).flatten(),
+                (dh[None].T * dw[None]).flatten(),
+            ]
+
+            for i in range(4):
+                idx_list[i].extend(indices[i].tolist())
+                weight_list[i].extend(weights[i].tolist())
+
+        idx_tensor = torch.tensor(idx_list, dtype=torch.long, device=self.pos_embed.weight.device)
+        weight_tensor = torch.tensor(
+            weight_list, dtype=self.pos_embed.weight.dtype, device=self.pos_embed.weight.device
+        )
+        pos_embeds = self.pos_embed(idx_tensor) * weight_tensor[:, :, None]
+        patch_pos_embeds = pos_embeds[0] + pos_embeds[1] + pos_embeds[2] + pos_embeds[3]
+
+        patch_pos_embeds = patch_pos_embeds.split([h * w for h, w in zip(grid_hs, grid_ws)])
+
+        patch_pos_embeds_permute = []
+        merge_size = self.config.spatial_merge_size
+        for pos_embed, t, h, w in zip(patch_pos_embeds, grid_ts, grid_hs, grid_ws):
+            pos_embed = pos_embed.repeat(t, 1)
+            pos_embed = (
+                pos_embed.view(t, h // merge_size, merge_size, w // merge_size, merge_size, -1)
+                .permute(0, 1, 3, 2, 4, 5)
+                .flatten(0, 4)
+            )
+            patch_pos_embeds_permute.append(pos_embed)
+        patch_pos_embeds = torch.cat(patch_pos_embeds_permute)
+        return patch_pos_embeds
+
+    def forward(
+        self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`):
+                The final hidden states of the model.
+            grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+                The temporal, height and width of feature shape of each image in LLM.
+
+        Returns:
+            `torch.Tensor`: hidden_states.
+        """
+        hidden_states = self.patch_embed(hidden_states)
+
+        pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
+        hidden_states = hidden_states + pos_embeds
+
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+        seq_len, _ = hidden_states.size()
+        hidden_states = hidden_states.reshape(seq_len, -1)
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        position_embeddings = (emb.cos(), emb.sin())
+
+        cu_seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+        ).cumsum(
+            dim=0,
+            # Select dtype based on the following factors:
+            #  - FA2 requires that cu_seqlens_q must have dtype int32
+            #  - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
+            # See https://github.com/huggingface/transformers/pull/34852 for more information
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        deepstack_feature_lists = []
+        for layer_num, blk in enumerate(self.blocks):
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+            if layer_num in self.deepstack_visual_indexes:
+                deepstack_feature = self.deepstack_merger_list[
+                    self.deepstack_visual_indexes.index(layer_num)
+                ](hidden_states)
+                deepstack_feature_lists.append(deepstack_feature)
+
+        # hidden_states = self.merger(hidden_states)
+
+        return hidden_states, deepstack_feature_lists
+
+
+class SMBVisionPredictor(PreTrainedModel):
+    config: SMBVisionPredictorConfig
+    _no_split_modules = ["SMBVisionBlock"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _can_compile_fullgraph = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+    _supports_attention_backend = True
+
+    def __init__(self, config, *inputs, **kwargs) -> None:
+        super().__init__(config, *inputs, **kwargs)
+
+        head_dim = config.hidden_size // config.num_heads
+        self.rotary_pos_emb = SMBVisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList(
+            [SMBVisionBlock(config) for _ in range(config.depth)]
+        )
+
+        self.in_proj = nn.Linear(config.in_hidden_size, config.hidden_size)
+        self.out_proj = nn.Linear(config.hidden_size, config.in_hidden_size)
+        self.mask_token = nn.Parameter(torch.zeros(config.hidden_size))
+        self.gradient_checkpointing = False
+
+    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        merge_size = 1
+
+        max_hw = int(grid_thw[:, 1:].max().item())
+        freq_table = self.rotary_pos_emb(max_hw)  # (max_hw, dim // 2)
+        device = freq_table.device
+
+        total_tokens = int(torch.prod(grid_thw, dim=1).sum().item())
+        pos_ids = torch.empty((total_tokens, 2), dtype=torch.long, device=device)
+
+        offset = 0
+        for num_frames, height, width in grid_thw:
+            merged_h, merged_w = height // merge_size, width // merge_size
+
+            block_rows = torch.arange(merged_h, device=device)  # block row indices
+            block_cols = torch.arange(merged_w, device=device)  # block col indices
+            intra_row = torch.arange(
+                merge_size, device=device
+            )  # intra-block row offsets
+            intra_col = torch.arange(
+                merge_size, device=device
+            )  # intra-block col offsets
+
+            # Compute full-resolution positions
+            row_idx = (
+                block_rows[:, None, None, None] * merge_size
+                + intra_row[None, None, :, None]
+            )
+            col_idx = (
+                block_cols[None, :, None, None] * merge_size
+                + intra_col[None, None, None, :]
+            )
+
+            row_idx = row_idx.expand(
+                merged_h, merged_w, merge_size, merge_size
+            ).reshape(-1)
+            col_idx = col_idx.expand(
+                merged_h, merged_w, merge_size, merge_size
+            ).reshape(-1)
+
+            coords = torch.stack((row_idx, col_idx), dim=-1)
+
+            if num_frames > 1:
+                coords = coords.repeat(num_frames, 1)
+
+            num_tokens = coords.shape[0]
+            pos_ids[offset : offset + num_tokens] = coords
+            offset += num_tokens
+
+        embeddings = freq_table[pos_ids]  # lookup rotary embeddings
+        embeddings = embeddings.flatten(1)
+        return embeddings
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thw: torch.Tensor,
+        target_mask: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        # mask out the hidden states
+        hidden_states = self.in_proj(hidden_states)
+        hidden_states[target_mask] = self.mask_token
+
+        # apply position embeddings
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        position_embeddings = (emb.cos(), emb.sin())
+
+        cu_seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+        ).cumsum(
+            dim=0,
+            # Select dtype based on the following factors:
+            #  - FA2 requires that cu_seqlens_q must have dtype int32
+            #  - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
+            # See https://github.com/huggingface/transformers/pull/34852 for more information
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        for layer_num, blk in enumerate(self.blocks):
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        # hidden_states = self.merger(hidden_states)
+        hidden_states = self.out_proj(hidden_states)
+
+        return hidden_states
+
+
+@dataclass
+class SMBVisionModelOutput(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    mim_loss: Optional[torch.FloatTensor] = None
+    jepa_loss: Optional[torch.FloatTensor] = None
+    hidden_states: Optional[torch.FloatTensor] = None
+    enc_hidden_states: Optional[torch.FloatTensor] = None
+    predicted_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class SMBVisionPretrainedModel(PreTrainedModel):
+    config: SMBVisionModelConfig
+    base_model_prefix = ""
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["SMBVisionBlock"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _can_compile_fullgraph = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+    _supports_attention_backend = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+
+        init_std = self.config.vision_config.initializer_range
+
+        # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
+        # `trunc_normal_cpu` not implemented in `half` issues
+        def trunc_normal_f32_(weight, std):
+            data_float_32 = weight.data.to(torch.float32)
+            data_init = nn.init.trunc_normal_(data_float_32, mean=0.0, std=std)
+            weight.data = data_init.to(weight.dtype)
+
+        if isinstance(module, SMBVisionEncoder):
+            trunc_normal_f32_(module.pos_embed.weight, std=init_std)
+            for i, layer in enumerate(module.blocks, 1):
+                std = init_std / (i**0.5)
+                trunc_normal_f32_(layer.attn.proj.weight, std=std)
+                trunc_normal_f32_(layer.mlp.fc2.weight, std=std)
+            std = init_std / (len(module.blocks) + 1) ** 0.5
+            trunc_normal_f32_(module.mlp.fc2.weight, std=std)
+        elif isinstance(module, SMBVisionPredictor):
+            trunc_normal_f32_(module.mask_token, std=init_std)
+            trunc_normal_f32_(module.in_proj.weight, std=init_std)
+            trunc_normal_f32_(module.out_proj.weight, std=init_std)
+            for i, layer in enumerate(module.blocks, 1):
+                std = init_std / (i**0.5)
+                trunc_normal_f32_(layer.attn.proj.weight, std=std)
+                trunc_normal_f32_(layer.mlp.fc2.weight, std=std)
+            std = init_std / (len(module.blocks) + 1) ** 0.5
+            trunc_normal_f32_(module.mlp.fc2.weight, std=std)
+        elif isinstance(module, (nn.Linear, nn.Conv2d, nn.Conv3d)):
+            trunc_normal_f32_(module.weight, std=init_std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+class SMBVisionModel(SMBVisionPretrainedModel):
+    def __init__(self, config, *inputs, **kwargs) -> None:
+        super().__init__(config, *inputs, **kwargs)
+
+        self.encoder = SMBVisionEncoder._from_config(config.vision_config)
+        self.predictor = SMBVisionPredictor._from_config(config.predictor_config)
+        self.to_pixels = nn.Linear(
+            config.vision_config.hidden_size,
+            config.vision_config.patch_size**2
+            * config.vision_config.temporal_patch_size,
+        )
+        self.masking_ratio = config.masking_ratio
+        self.mask_token = nn.Parameter(
+            torch.zeros(
+                config.vision_config.in_channels
+                * config.vision_config.temporal_patch_size
+                * config.vision_config.patch_size**2
+            )
+        )
+
+        self.mim_loss = nn.L1Loss(reduction="mean")
+        self.jepa_loss = nn.MSELoss(reduction="mean")
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward_features(
+        self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs
+    ) -> torch.Tensor:
+        return self.encoder(hidden_states, grid_thw, **kwargs)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thw: torch.Tensor,
+        context_mask: Optional[torch.Tensor],
+        target_mask: Optional[torch.Tensor],
+        **kwargs,
+    ) -> torch.Tensor:
+        # modeling masked image reconstruction
+        # prepare mask tokens
+        num_masked = int(self.masking_ratio * hidden_states.shape[0])
+        masked_indices = torch.randperm(hidden_states.shape[0])[:num_masked]
+        # replace masked indices with mask tokens
+        inputs_mim = hidden_states.clone()
+        inputs_mim[masked_indices] = self.mask_token.to(hidden_states.dtype)
+        masked_hidden_states, deepstack_feature_lists = self.encoder(
+            inputs_mim, grid_thw, **kwargs
+        )
+        masked_hidden_states = self.to_pixels(masked_hidden_states)
+        # compute mim loss
+        mim_loss = self.mim_loss(
+            masked_hidden_states[masked_indices], hidden_states[masked_indices]
+        )
+
+        # modeling next embedding prediction
+        if context_mask is not None and target_mask is not None:
+            context_mask = context_mask == 1
+            target_mask = target_mask == 1
+            # extend context and target masks
+            lengths = torch.prod(grid_thw, dim=1)
+            extended_context_mask = torch.repeat_interleave(context_mask, lengths)
+            extended_target_mask = torch.repeat_interleave(target_mask, lengths)
+
+            enc_hidden_states, deepstack_feature_lists = self.encoder(
+                hidden_states[extended_context_mask], grid_thw[context_mask], **kwargs
+            )
+            pred_hidden_states = self.predictor(
+                enc_hidden_states,
+                grid_thw[context_mask],
+                extended_target_mask,
+                **kwargs,
+            )
+            jepa_loss = self.jepa_loss(
+                pred_hidden_states[extended_target_mask],
+                enc_hidden_states[extended_target_mask],
+            )
+
+            loss = mim_loss + jepa_loss
+            return SMBVisionModelOutput(
+                loss=loss,
+                mim_loss=mim_loss,
+                jepa_loss=jepa_loss,
+                hidden_states=hidden_states,
+                enc_hidden_states=enc_hidden_states,
+                predicted_hidden_states=pred_hidden_states,
+            )
+        else:
+            return SMBVisionModelOutput(
+                loss=mim_loss,
+                mim_loss=mim_loss,
+                jepa_loss=None,
+                hidden_states=hidden_states,
+                predicted_hidden_states=None,
+            )
+
+
+__all__ = ["SMBVisionEncoder", "SMBVisionPredictor", "SMBVisionModel"]

training_args.bin

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