chore: import upstream snapshot with attribution

python/sglang/srt/models/kimi_vl.py

@@ -0,0 +1,329 @@
+# SPDX-License-Identifier: Apache-2.0
+# ruff: noqa: E501
+# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/modeling_kimi_vl.py
+# Copyright 2025 The Moonshot AI Team, DeepSeek-AI, and HuggingFace Inc. team. All rights reserved.
+#
+# The code is based on llava (llava/modeling_llava.py) and DeepSeek-V3 (DeepSeek-V3/modeling_deepseek.py), but modified for KimiVL.
+#
+# Licensing Information:
+# - Code derived from llava (llava/modeling_llava.py) and DeepSeek-V3 (DeepSeek-V3/modeling_deepseek.py) is licensed under the Apache License, Version 2.0.
+# - Other parts of the code are licensed under the MIT License.
+#
+# Apache License, Version 2.0:
+# 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.
+#
+# MIT License:
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import copy
+import logging
+from dataclasses import dataclass
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers.activations import GELUActivation
+
+from sglang.srt.configs import KimiVLConfig
+from sglang.srt.configs.deepseekvl2 import DeepseekV2Config
+from sglang.srt.configs.kimi_vl import KimiVLConfig
+from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
+from sglang.srt.layers.activation import QuickGELU
+from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternMultimodalTokens,
+    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,
+    maybe_remap_kv_scale_name,
+)
+from sglang.srt.models.deepseek_v2 import DeepseekV2ForCausalLM
+from sglang.srt.models.kimi_vl_moonvit import MoonVitPretrainedModel
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+
+# For dummy input only
+@dataclass
+class MaxImageTokenMeta:
+    width: int = 1024
+    height: int = 1024
+
+
+class KimiVLMultiModalProjector(nn.Module):
+
+    def __init__(self, config: KimiVLConfig):
+        super().__init__()
+
+        self.hidden_size = (
+            config.vision_config.hidden_size
+            * config.vision_config.merge_kernel_size[0]
+            * config.vision_config.merge_kernel_size[1]
+        )
+
+        self.pre_norm = torch.nn.LayerNorm(config.vision_config.hidden_size, eps=1e-5)
+        self.linear_1 = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
+        self.act = GELUActivation()
+        self.act = QuickGELU()
+        self.linear_2 = nn.Linear(
+            self.hidden_size, config.text_config.hidden_size, bias=True
+        )
+
+    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class KimiVLForConditionalGeneration(nn.Module):
+    def __init__(
+        self,
+        config: KimiVLConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        **kwargs,  # fix init_tts argument error
+    ) -> None:
+        super().__init__()
+        self.config = config
+        assert isinstance(config.vision_config, MoonViTConfig)
+
+        self.vision_tower = MoonVitPretrainedModel(config.vision_config)
+
+        self.multi_modal_projector = KimiVLMultiModalProjector(config=config)
+        self.quant_config = quant_config
+
+        self.language_model = None
+        if not config.encoder_only:
+            text_config = copy.deepcopy(config.text_config)
+            text_config.architectures = ["DeepseekV2ForCausalLM"]
+            self.language_model = DeepseekV2ForCausalLM(
+                config=text_config,
+                quant_config=quant_config,
+                prefix=add_prefix("language_model", prefix),
+            )
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        pixel_values = (
+            torch.cat([item.feature for item in items], dim=0)
+            .type(self.vision_tower.dtype)
+            .to(self.vision_tower.device)
+        )
+
+        if (
+            pixel_values.dim() == 2
+            and pixel_values.shape[-1] == self.config.text_config.hidden_size
+        ):
+            return pixel_values
+
+        image_grid_hws = torch.cat([item.image_grid_hws for item in items], dim=0).to(
+            self.vision_tower.device
+        )
+        image_features = self.vision_tower(pixel_values, image_grid_hws)
+        assert isinstance(image_features, list)
+        # lengths = [x.shape[0] for x in image_features]
+        res = self.multi_modal_projector(torch.cat(image_features))  # .split(lengths)
+        return res
+
+    def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+        pattern = MultiModalityDataPaddingPatternMultimodalTokens()
+        return pattern.pad_input_tokens(input_ids, mm_inputs)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        get_embedding: bool = False,
+    ):
+        hidden_states = 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 hidden_states
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        config = self.config.text_config
+        _KEYS_TO_MODIFY_MAPPING = {
+            # "language_model.lm_head": "lm_head",
+            # "language_model.model": "language_model",
+        }
+        # only doing this for language model part for now.
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        if not config.use_mla:
+            stacked_params_mapping += [
+                (".qkv_proj", ".q_proj", "q"),
+                (".qkv_proj", ".k_proj", "k"),
+                (".qkv_proj", ".v_proj", "v"),
+            ]
+        if getattr(config, "n_routed_experts", None):
+            # Params for weights, fp8 weight scales, fp8 activation scales
+            # (param_name, weight_name, expert_id, shard_id)
+            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=config.n_routed_experts,
+            )
+        else:
+            expert_params_mapping = []
+
+        params_dict = dict(self.named_parameters())
+        for args in weights:
+            name, loaded_weight = args[:2]
+            kwargs = args[2] if len(args) > 2 else {}
+
+            is_vision_weight = ("vision" in name) or ("multi_modal_projector" in name)
+            if self.config.encoder_only and not is_vision_weight:
+                continue
+            if self.config.language_only and is_vision_weight:
+                continue
+
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            spec_layer = get_spec_layer_idx_from_weight_name(config, name)
+            if spec_layer is not None:
+                continue  # skip spec decode layers for main model
+
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
+                if key_to_modify in name:
+                    name = name.replace(key_to_modify, new_key)
+            use_default_weight_loading = False
+            if "vision" in name:
+                if self.vision_tower is not None:
+                    # We only do sharding for language model and
+                    # not vision model for now.
+                    use_default_weight_loading = True
+            else:
+                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) and name not in params_dict:
+                        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
+                    if name not in params_dict:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id, **kwargs)
+                    break
+                else:
+                    for idx, (
+                        param_name,
+                        weight_name,
+                        expert_id,
+                        shard_id,
+                    ) in enumerate(expert_params_mapping):
+                        if weight_name not in name:
+                            continue
+                        name = name.replace(weight_name, param_name)
+                        if name not in params_dict:
+                            continue
+
+                        param = params_dict[name]
+                        weight_loader = param.weight_loader
+                        weight_loader(
+                            param,
+                            loaded_weight,
+                            name,
+                            expert_id=expert_id,
+                            shard_id=shard_id,
+                            **kwargs,
+                        )
+                        break
+                    else:
+                        use_default_weight_loading = True
+            if use_default_weight_loading:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+
+                # if is_pp_missing_parameter(name, self):
+                #     continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight, **kwargs)
+        if self.language_model is not None:
+            self.language_model.post_load_weights()
+
+
+def get_spec_layer_idx_from_weight_name(
+    config: DeepseekV2Config, weight_name: str
+) -> Optional[int]:
+    if hasattr(config, "num_nextn_predict_layers") and (
+        config.num_nextn_predict_layers > 0
+    ):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_nextn_predict_layers):
+            if weight_name.startswith(f"model.layers.{layer_idx+i}."):
+                return layer_idx + i
+    return None
+
+
+EntryClass = [KimiVLForConditionalGeneration]