support dynamic resolution image encoding for Nemotron Nano VL (#32121)

Signed-off-by: Netanel Haber <58652339+netanel-haber@users.noreply.github.com>

vllm/model_executor/models/intern_vit.py

@@ -282,12 +282,14 @@ class InternVisionEncoderLayer(nn.Module):
         num_dummy_heads: int = 0,
         prefix: str = "",
         use_data_parallel: bool = False,
+        attn_cls: type[InternParallelAttention] = InternParallelAttention,
     ) -> None:
         super().__init__()
 
         self.embed_dim = config.hidden_size
         self.intermediate_size = config.intermediate_size
         self.norm_type = config.norm_type
+        self.attn_cls = attn_cls
 
         self.attn = self._init_attn(
             config,
@@ -327,7 +329,7 @@ class InternVisionEncoderLayer(nn.Module):
         use_data_parallel = (
             use_data_parallel or (num_heads + num_dummy_heads) % tp_size != 0
         )
-        return InternParallelAttention(
+        return self.attn_cls(
             config,
             quant_config=quant_config,
             num_dummy_heads=num_dummy_heads,
@@ -356,10 +358,12 @@ class InternVisionEncoder(nn.Module):
         num_dummy_heads: int = 0,
         prefix: str = "",
         use_data_parallel: bool = False,
+        layer_cls: type[InternVisionEncoderLayer] = InternVisionEncoderLayer,
     ):
         super().__init__()
 
         self.config = config
+        self.layer_cls = layer_cls
 
         if num_hidden_layers_override is None:
             num_hidden_layers = config.num_hidden_layers
@@ -368,7 +372,7 @@ class InternVisionEncoder(nn.Module):
 
         self.layers = nn.ModuleList(
             [
-                InternVisionEncoderLayer(
+                self.layer_cls(
                     config,
                     quant_config,
                     num_dummy_heads=num_dummy_heads,

vllm/model_executor/models/radio.py

@@ -21,7 +21,11 @@ from transformers import PretrainedConfig
 
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.models.intern_vit import InternVisionEncoder
+from vllm.model_executor.models.intern_vit import (
+    InternParallelAttention,
+    InternVisionEncoder,
+    InternVisionEncoderLayer,
+)
 
 input_dim_t: TypeAlias = int | tuple[int, int]
 norm_t: TypeAlias = tuple[float, float, float] | torch.Tensor
@@ -43,6 +47,15 @@ to_4tuple = _ntuple(4)
 to_ntuple = _ntuple
 
 
+def calc_seq_len(size: tuple[int, int], patch_size: int) -> int:
+    h, w = size
+    return (h // patch_size) * (w // patch_size)
+
+
+def calc_seq_lens(sizes: list[tuple[int, int]], patch_size: int) -> list[int]:
+    return [calc_seq_len(size, patch_size) for size in sizes]
+
+
 class ClsToken(nn.Module):
     def __init__(
         self,
@@ -164,15 +177,73 @@ class ViTPatchGenerator(nn.Module):
             nn.LayerNorm(embed_dim) if normalize_patches else nn.Identity()
         )
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        patches = self.embed_patches(x)
-        patches, pos_enc = self.apply_pos_enc(patches, input_size=x.shape[2:])
-        patches = self.cls_token(patches)
+    def forward(
+        self, x: torch.Tensor, imgs_sizes: list[tuple[int, int]] | None = None
+    ) -> torch.Tensor:
+        if imgs_sizes is not None:
+            patches = self.embedder(x)
+            patches, pos_enc = self.apply_pos_enc_dynamic(
+                patches, imgs_sizes=imgs_sizes
+            )
+            patches = self.cls_token_dynamic(patches, imgs_sizes=imgs_sizes)
+        else:
+            patches = self.embed_patches(x)
+            patches, pos_enc = self.apply_pos_enc(patches, input_size=x.shape[2:])
+            patches = self.cls_token(patches)
         patches = self.patch_normalizer(patches)
         if self.return_pos_enc:
             return patches, pos_enc
         return patches
 
+    def apply_pos_enc_dynamic(
+        self, patches: torch.Tensor, imgs_sizes: list[tuple[int, int]]
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        if not self.abs_pos:
+            return patches, None
+
+        current_length = 0
+        pos_enc_list = []
+
+        for size in imgs_sizes:
+            seq_length = calc_seq_len(size, self.patch_size)
+
+            img_patches = patches[:, current_length : current_length + seq_length, :]
+            pos_enc = self.get_pos_enc(patches.shape[0], input_size=size)
+            img_patches_with_pos = img_patches + pos_enc
+
+            patches = torch.cat(
+                [
+                    patches[:, :current_length, :],
+                    img_patches_with_pos,
+                    patches[:, current_length + seq_length :, :],
+                ],
+                dim=1,
+            )
+            pos_enc_list.append(pos_enc)
+            current_length += seq_length
+
+        full_pos_enc = torch.cat(pos_enc_list, dim=1) if pos_enc_list else None
+        return patches, full_pos_enc
+
+    def cls_token_dynamic(
+        self, patches: torch.Tensor, imgs_sizes: list[tuple[int, int]]
+    ) -> torch.Tensor:
+        if not self.cls_token.enabled:
+            return patches
+
+        out = []
+        current_length = 0
+
+        for seq_len in calc_seq_lens(imgs_sizes, self.patch_size):
+            class_token = self.cls_token.token.unsqueeze(0).expand(
+                patches.shape[0], -1, -1
+            )
+            out.append(class_token)
+            out.append(patches[:, current_length : current_length + seq_len, :])
+            current_length += seq_len
+
+        return torch.cat(out, dim=1)
+
     @property
     def apply_cls_token(self):
         return self.cls_token.enabled
@@ -406,6 +477,66 @@ class ViTPatchLinear(nn.Linear):
         self.patch_size = patch_size
 
 
+class RadioParallelAttention(InternParallelAttention):
+    def forward(
+        self, x: torch.Tensor, attn_mask: torch.Tensor | None = None
+    ) -> torch.Tensor:
+        if attn_mask is None:
+            return super().forward(x)
+
+        B, N, _ = x.shape
+        qkv, _ = self.qkv(x)
+        q, k, v = qkv.chunk(3, dim=-1)
+
+        if self.qk_normalization:
+            q, k = self._apply_qk_norm(q, k)
+
+        q = q.view(B, N, self.num_heads_per_partition, self.head_dim)
+        k = k.view(B, N, self.num_heads_per_partition, self.head_dim)
+        v = v.view(B, N, self.num_heads_per_partition, self.head_dim)
+        q, k, v = (t.transpose(1, 2) for t in (q, k, v))
+        out = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=attn_mask, scale=self.scale
+        )
+        out = out.transpose(1, 2).reshape(B, N, -1)
+        out, _ = self.proj(out)
+        return out
+
+
+class RadioVisionEncoderLayer(InternVisionEncoderLayer):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, attn_cls=RadioParallelAttention, **kwargs)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
+    ):
+        hidden_states = (
+            hidden_states
+            + self.attn(self.norm1(hidden_states), attn_mask=attn_mask) * self.ls1
+        )
+
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) * self.ls2
+
+        return hidden_states
+
+
+class RadioVisionEncoder(InternVisionEncoder):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, layer_cls=RadioVisionEncoderLayer, **kwargs)
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
+    ):
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states, attn_mask=attn_mask)
+        return hidden_states
+
+
 class RadioInternVisionModel(nn.Module):
     packed_modules_mapping = {
         "qkv": ["qkv"],
@@ -440,7 +571,7 @@ class RadioInternVisionModel(nn.Module):
             register_multiple=config.register_multiple,
         )
 
-        self.encoder = InternVisionEncoder(
+        self.encoder = RadioVisionEncoder(
             config=config,
             quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers_override,
@@ -459,10 +590,45 @@ class RadioInternVisionModel(nn.Module):
     def get_input_embeddings(self):
         return self.embeddings
 
-    def forward(self, x: torch.Tensor) -> torch.FloatTensor:
+    def create_inter_image_attention_mask(
+        self, imgs_sizes: list[tuple[int, int]], device: torch.device
+    ) -> torch.Tensor:
+        patch_size = self.patch_generator.patch_size
+        num_skip = self.patch_generator.num_skip
+
+        seq_lens = calc_seq_lens(imgs_sizes, patch_size)
+        patch_counts = [seq_len + num_skip for seq_len in seq_lens]
+        total_patches = sum(patch_counts)
+
+        # Create attention mask - default to False (mask out)
+        mask = torch.zeros(
+            total_patches, total_patches, dtype=torch.bool, device=device
+        )
+
+        # Each image's patches can only attend to patches from the same image
+        start_idx = 0
+        for patch_count in patch_counts:
+            end_idx = start_idx + patch_count
+            # Allow attention within this image's patches
+            mask[start_idx:end_idx, start_idx:end_idx] = True
+            start_idx = end_idx
+
+        return mask
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        imgs_sizes: torch.Tensor | None = None,
+    ) -> torch.FloatTensor:
         assert self.patch_generator is not None
-        hidden_states = self.patch_generator(x)
-        encoder_outputs = self.encoder(inputs_embeds=hidden_states)
+        hidden_states = self.patch_generator(x, imgs_sizes=imgs_sizes)
+        attn_mask = None
+        if imgs_sizes is not None and len(imgs_sizes) > 1:
+            # Dynamic Resolution
+            attn_mask = self.create_inter_image_attention_mask(
+                imgs_sizes, device=x.device
+            )
+        encoder_outputs = self.encoder(inputs_embeds=hidden_states, attn_mask=attn_mask)
         return encoder_outputs
 
 
@@ -504,9 +670,11 @@ class RadioModel(nn.Module):
         self,
         pixel_values: torch.Tensor | None = None,
         pixel_embeds: torch.Tensor | None = None,
+        *,
+        imgs_sizes: torch.Tensor | None = None,
     ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
-        y = self.model(pixel_values)
-        return self._extract_final(y)
+        y = self.model(pixel_values, imgs_sizes=imgs_sizes)
+        return self._extract_final(y, imgs_sizes=imgs_sizes)
 
     def load_weights(self, weights) -> set[str]:
         loaded_params: set[str] = set()
@@ -558,16 +726,32 @@ class RadioModel(nn.Module):
         return loaded_params
 
     def _extract_final(
-        self, y: torch.Tensor
+        self, y: torch.Tensor, imgs_sizes: list[tuple[int, int]] | None = None
     ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
         # Remove CLS + REGISTERS tokens
-        patch_gen = getattr(self.model, "patch_generator", None)
-        if patch_gen is not None:
-            all_summary = y[:, : patch_gen.num_cls_tokens]
-            if self.summary_idxs is not None:
-                bb_summary = all_summary[:, self.summary_idxs]
-            else:
-                bb_summary = all_summary
-            all_feat = y[:, patch_gen.num_skip :]
-
+        num_skip = self.model.patch_generator.num_skip
+        patch_size = self.model.patch_generator.patch_size
+        num_cls_tokens = self.model.patch_generator.num_cls_tokens
+        if imgs_sizes is None:
+            all_summary = y[:, :num_cls_tokens]
+            all_feat = y[:, num_skip:]
+        else:
+            all_patches = []
+            summaries = []
+            current_pos = 0
+            for num_patches in calc_seq_lens(imgs_sizes, patch_size):
+                patches = y[
+                    :, current_pos + num_skip : current_pos + num_skip + num_patches, :
+                ]
+                all_patches.append(patches)
+                summary = y[:, current_pos : current_pos + num_cls_tokens, :]
+                summaries.append(summary)
+                current_pos += num_skip + num_patches
+            all_summary = torch.cat(summaries, dim=1)
+            all_feat = torch.cat(all_patches, dim=1)
+
+        if self.summary_idxs is not None:
+            bb_summary = all_summary[:, self.summary_idxs]
+        else:
+            bb_summary = all_summary
         return bb_summary.flatten(1), all_feat