chore: import upstream snapshot with attribution

python/sglang/srt/models/interns1.py

@@ -0,0 +1,274 @@
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.layers.attention import vision_utils
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternTokenPairs,
+    general_mm_embed_routine,
+)
+from sglang.srt.managers.schedule_batch import (
+    Modality,
+    MultimodalDataItem,
+    MultimodalInputs,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.internvl import InternVisionModel
+from sglang.srt.models.qwen2 import Qwen2ForCausalLM
+from sglang.srt.models.qwen3 import Qwen3ForCausalLM
+from sglang.srt.models.qwen3_moe import Qwen3MoeForCausalLM
+from sglang.utils import logger
+
+
+class InternS1ForConditionalGeneration(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        use_flash_attn=True,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        vision_utils.update_vit_attn_dummy_heads_config(self.config)
+        image_size = (
+            getattr(config, "force_image_size", None) or config.vision_config.image_size
+        )
+        patch_size = config.vision_config.patch_size
+        if isinstance(image_size, list):
+            image_size = image_size[0]
+        if isinstance(patch_size, list):
+            patch_size = patch_size[0]
+        self.patch_size = patch_size
+        self.select_layer = config.vision_feature_layer
+        self.num_image_token = int(
+            (image_size // patch_size) ** 2 * (config.downsample_ratio**2)
+        )
+        self.downsample_ratio = config.downsample_ratio
+
+        config.vision_config.use_flash_attn = True if use_flash_attn else False
+        config.text_config._attn_implementation = (
+            "flash_attention_2" if use_flash_attn else "eager"
+        )
+
+        logger.info(f"num_image_token: {self.num_image_token}")
+
+        self.vision_model = InternVisionModel(config.vision_config)
+        if config.text_config.architectures[0] == "Qwen2ForCausalLM":
+            self.language_model = Qwen2ForCausalLM(
+                config=config.text_config, quant_config=quant_config
+            )
+        elif config.text_config.architectures[0] == "Qwen3MoeForCausalLM":
+            self.language_model = Qwen3MoeForCausalLM(
+                config=config.text_config, quant_config=quant_config
+            )
+        elif config.text_config.architectures[0] == "Qwen3ForCausalLM":
+            self.language_model = Qwen3ForCausalLM(
+                config=config.text_config, quant_config=quant_config
+            )
+        else:
+            raise NotImplementedError(
+                f"{config.text_config.architectures[0]} is not implemented."
+            )
+
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.text_config.hidden_size
+
+        self.mlp1 = nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
+            nn.Linear(
+                vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size
+            ),
+            nn.GELU(),
+            nn.Linear(llm_hidden_size, llm_hidden_size),
+        )
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+        x = x.view(
+            n,
+            int(h * scale_factor),
+            int(w * scale_factor),
+            int(c / (scale_factor * scale_factor)),
+        )
+        x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values):
+        if self.select_layer == -1:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values, output_hidden_states=False, return_dict=True
+            ).last_hidden_state
+        else:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values, output_hidden_states=True, return_dict=True
+            ).hidden_states[self.select_layer]
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        h = w = int(vit_embeds.shape[1] ** 0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    def get_image_feature(self, items: List[MultimodalDataItem]):
+        """
+        Projects the last hidden state from the vision model into language model space.
+
+        Returns:
+            image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+        """
+        pixel_values = torch.cat([item.feature for item in items])
+        image_features = self.extract_feature(pixel_values)
+        return image_features
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+
+        hs = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            language_model=self.language_model,
+            data_embedding_funcs={
+                Modality.IMAGE: self.get_image_feature,
+            },
+            positions=positions,
+        )
+
+        return hs
+
+    def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+        # Get all special token IDs
+        im_start_id: int = mm_inputs.im_start_id
+        im_end_id: int = mm_inputs.im_end_id
+
+        media_token_pairs = [(im_start_id, im_end_id)]
+        helper = MultiModalityDataPaddingPatternTokenPairs(media_token_pairs)
+
+        return helper.pad_input_tokens(input_ids, mm_inputs)
+
+    def _mapping_interns1_name(self, name):
+        names_map = {
+            "lm_head.weight": "language_model.lm_head.weight",
+            "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
+            "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
+            "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
+            "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
+            "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
+            "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
+            "model.vision_tower.embeddings.cls_token": "vision_model.embeddings.class_embedding",
+            "model.vision_tower.embeddings.patch_embeddings.projection.bias": "vision_model.embeddings.patch_embedding.bias",
+            "model.vision_tower.embeddings.patch_embeddings.projection.weight": "vision_model.embeddings.patch_embedding.weight",
+            "model.vision_tower.embeddings.position_embeddings": "vision_model.embeddings.position_embedding",
+        }
+        if name in names_map:
+            name = names_map[name]
+        elif name.startswith("model.language_model."):
+            name = "language_model.model." + name[len("model.language_model.") :]
+        elif name.startswith("model.vision_tower."):
+            name = "vision_model." + name[len("model.vision_tower.") :]
+
+        if name.startswith("vision_model.encoder.layer"):
+
+            name = name.replace(r".layer.", r".layers.")
+            name = name.replace(r".attention.", r".attn.attn.")
+            name = name.replace(r".projection_layer.", r".proj.")
+            name = name.replace(r".lambda_1", r".ls1")
+            name = name.replace(r".lambda_2", r".ls2")
+            name = name.replace(r".layernorm_before.", r".norm1.")
+            name = name.replace(r".layernorm_after.", r".norm2.")
+        return name
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        expert_params_mapping = []
+        if "Qwen3MoeForCausalLM" in self.config.text_config.architectures:
+            expert_params_mapping = FusedMoE.make_expert_params_mapping(
+                ckpt_gate_proj_name="gate_proj",
+                ckpt_down_proj_name="down_proj",
+                ckpt_up_proj_name="up_proj",
+                num_experts=self.config.text_config.num_experts,
+            )
+
+        params_dict = dict(self.named_parameters())
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            name = self._mapping_interns1_name(name)
+            if "vision_model" in name:
+                loaded_weight = vision_utils.pad_vit_attn_dummy_heads(
+                    self.config, name, loaded_weight
+                )
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if "mlp.experts" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+
+EntryClass = InternS1ForConditionalGeneration