sglang/python/sglang/srt/models/internvl.py

# Copyright 2023-2024 SGLang Team
# 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.
# ==========================582====================================================

from typing import Iterable, List, Optional, Tuple, Union

import torch

# Adapted from https://raw.githubusercontent.com/vllm-project/vllm/7f62077af5159c625fe3ad1c812e6c1a2b93ba3b/vllm/model_executor/models/internlm2.py
# Adapted from https://raw.githubusercontent.com/hehesangsj/sglang/refs/heads/internvl/python/sglang/srt/models/internvl.py
import torch.nn.functional as F
from einops import rearrange, repeat
from sgl_kernel.flash_attn import flash_attn_varlen_func
from torch import nn
from transformers import PretrainedConfig, PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling

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 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.deepseek_janus_pro import DropPath
from sglang.srt.models.internlm2 import InternLM2ForCausalLM
from sglang.srt.models.qwen2 import Qwen2ForCausalLM
from sglang.utils import logger


class FlashAttention(nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_scale: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.0)
    """

    def __init__(
        self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None
    ):
        super().__init__()
        self.softmax_scale = softmax_scale
        self.dropout_p = attention_dropout

    def forward(
        self,
        qkv,
        causal=False,
        max_s=None,
    ):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
                if unpadded: (nnz, 3, h, d)
        """
        assert qkv.dtype in [torch.float16, torch.bfloat16]
        assert qkv.is_cuda

        batch_size, seqlen, _, nheads, d = qkv.shape
        if batch_size == 0 or seqlen == 0:
            output_shape = (batch_size, seqlen, nheads, d)
            return (
                torch.zeros(output_shape, dtype=qkv.dtype, device=qkv.device),
                None,
            )

        qkv_reshaped = rearrange(qkv, "b s three h d -> (b s) three h d", three=3)
        q, k, v = qkv_reshaped.unbind(1)

        max_s = seqlen
        cu_seqlens = torch.arange(
            0,
            (batch_size + 1) * seqlen,
            step=seqlen,
            dtype=torch.int32,
            device=qkv.device,
        )
        output_reshaped = flash_attn_varlen_func(
            q,
            k,
            v,
            cu_seqlens,
            cu_seqlens,
            max_s,
            max_s,
            softmax_scale=self.softmax_scale,
            causal=causal,
        )
        output = rearrange(output_reshaped, "(b s) h d -> b s h d", b=batch_size)
        return output, None


class InternAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads

        self.scale = self.head_dim**-0.5
        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
        self.proj_drop = nn.Dropout(config.dropout)

        self.qk_normalization = config.qk_normalization

        if self.qk_normalization:
            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)

        self.inner_attn = FlashAttention(softmax_scale=self.scale)

        self.proj = nn.Linear(self.embed_dim, self.embed_dim)

    def _flash_attn(
        self,
        x,
    ):
        qkv = self.qkv(x)
        qkv = rearrange(
            qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads
        )

        if self.qk_normalization:
            q, k, v = qkv.unbind(2)
            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
            qkv = torch.stack([q, k, v], dim=2)

        context, _ = self.inner_attn(
            qkv,
        )
        outs = self.proj(rearrange(context, "b s h d -> b s (h d)"))
        outs = self.proj_drop(outs)
        return outs

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        x = self._flash_attn(hidden_states)
        return x


class InternVisionEmbeddings(nn.Module):
    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.class_embedding = nn.Parameter(
            torch.randn(1, 1, self.embed_dim),
        )

        self.patch_embedding = nn.Conv2d(
            in_channels=3,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
        )

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1

        self.position_embedding = nn.Parameter(
            torch.randn(1, self.num_positions, self.embed_dim)
        )

    def _get_pos_embed(self, pos_embed, H, W):
        target_dtype = pos_embed.dtype
        pos_embed = (
            pos_embed.float()
            .reshape(
                1,
                self.image_size // self.patch_size,
                self.image_size // self.patch_size,
                -1,
            )
            .permute(0, 3, 1, 2)
        )
        pos_embed = (
            F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False)
            .reshape(1, -1, H * W)
            .permute(0, 2, 1)
            .to(target_dtype)
        )
        return pos_embed

    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(
            pixel_values
        )  # shape = [*, channel, width, height]
        batch_size, _, height, width = patch_embeds.shape
        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
        position_embedding = torch.cat(
            [
                self.position_embedding[:, :1, :],
                self._get_pos_embed(self.position_embedding[:, 1:, :], height, width),
            ],
            dim=1,
        )
        embeddings = embeddings + position_embedding.to(target_dtype)
        return embeddings


class InternRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)


class InternMLP(nn.Module):
    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.config = config
        self.act = ACT2FN[config.hidden_act]
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states


NORM2FN = {
    "rms_norm": InternRMSNorm,
    "layer_norm": nn.LayerNorm,
}


class InternVisionEncoderLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        drop_path_rate: float,
        quant_config: QuantizationConfig = None,
    ):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.norm_type = config.norm_type
        self.attn = InternAttention(config)
        self.mlp = InternMLP(config)
        self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
        self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)

        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
        self.drop_path1 = (
            DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
        )
        self.drop_path2 = (
            DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> Tuple[
        torch.FloatTensor,
        Optional[torch.FloatTensor],
        Optional[Tuple[torch.FloatTensor]],
    ]:
        """
        Args:
            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
        """
        hidden_states = hidden_states + self.drop_path1(
            self.attn(self.norm1(hidden_states).to(hidden_states.dtype)) * self.ls1
        )

        hidden_states = hidden_states + self.drop_path2(
            self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * self.ls2
        )

        return hidden_states


class InternVisionEncoder(nn.Module):
    """
    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
    [`InternEncoderLayer`].

    Args:
        config (`InternConfig`):
            The corresponding vision configuration for the `InternEncoder`.
    """

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        # stochastic depth decay rule
        dpr = [
            x.item()
            for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)
        ]
        self.layers = nn.ModuleList(
            [
                InternVisionEncoderLayer(config, dpr[idx], quant_config)
                for idx in range(config.num_hidden_layers)
            ]
        )

    def forward(
        self,
        inputs_embeds,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Embedded representation of the inputs. Should be float, not int tokens.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        encoder_states = () if output_hidden_states else None
        hidden_states = inputs_embeds

        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            layer_outputs = encoder_layer(
                hidden_states,
            )
            hidden_states = layer_outputs

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=encoder_states
        )


class InternVisionModel(PreTrainedModel):
    main_input_name = "pixel_values"
    _supports_flash_attn_2 = True
    config_class = PretrainedConfig
    _no_split_modules = ["InternVisionEncoderLayer"]

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__(config)
        self.config = config

        self.embeddings = InternVisionEmbeddings(
            config,
        )
        self.encoder = InternVisionEncoder(config, quant_config)

    def resize_pos_embeddings(self, old_size, new_size, patch_size):
        pos_emb = self.embeddings.position_embedding
        _, num_positions, embed_dim = pos_emb.shape
        cls_emb = pos_emb[:, :1, :]
        pos_emb = (
            pos_emb[:, 1:, :]
            .reshape(1, old_size // patch_size, old_size // patch_size, -1)
            .permute(0, 3, 1, 2)
        )
        pos_emb = F.interpolate(
            pos_emb.float(),
            size=new_size // patch_size,
            mode="bicubic",
            align_corners=False,
        )
        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
        self.embeddings.position_embedding = nn.Parameter(pos_emb)
        self.embeddings.image_size = new_size
        logger.info(
            "Resized position embeddings from {} to {}".format(old_size, new_size)
        )

    def get_input_embeddings(self):
        return self.embeddings

    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        pixel_embeds: Optional[torch.FloatTensor] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        pixel_values = pixel_values.to(device=self.device, dtype=self.dtype)
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        if pixel_values is None and pixel_embeds is None:
            raise ValueError("You have to specify pixel_values or pixel_embeds")

        if pixel_embeds is not None:
            hidden_states = pixel_embeds
        else:
            if len(pixel_values.shape) == 4:
                hidden_states = self.embeddings(pixel_values)
            else:
                raise ValueError(f"wrong pixel_values size: {pixel_values.shape}")
        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        last_hidden_state = encoder_outputs.last_hidden_state
        pooled_output = last_hidden_state[:, 0, :]

        if not return_dict:
            return (last_hidden_state, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class InternVLChatModel(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

        image_size = config.force_image_size or config.vision_config.image_size
        patch_size = config.vision_config.patch_size
        self.patch_size = patch_size
        self.select_layer = config.select_layer
        self.template = config.template
        self.num_image_token = int(
            (image_size // patch_size) ** 2 * (config.downsample_ratio**2)
        )
        self.downsample_ratio = config.downsample_ratio
        self.ps_version = config.ps_version

        config.vision_config.use_flash_attn = True if use_flash_attn else False
        config.llm_config._attn_implementation = (
            "flash_attention_2" if use_flash_attn else "eager"
        )

        logger.info(f"num_image_token: {self.num_image_token}")
        logger.info(f"ps_version: {self.ps_version}")

        self.vision_model = InternVisionModel(config.vision_config)
        if config.llm_config.architectures[0] == "Qwen2ForCausalLM":
            self.language_model = Qwen2ForCausalLM(
                config=config.llm_config, quant_config=quant_config
            )
        elif config.llm_config.architectures[0] == "InternLM2ForCausalLM":
            self.language_model = InternLM2ForCausalLM(
                config=config.llm_config, quant_config=quant_config
            )
        else:
            raise NotImplementedError(
                f"{config.llm_config.architectures[0]} is not implemented."
            )

        vit_hidden_size = config.vision_config.hidden_size
        llm_hidden_size = config.llm_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)),
        )
        if self.ps_version == "v1":
            logger.warn(
                "In ps_version 'v1', the height and width have not been swapped back, "
                "which results in a transposed image."
            )
        else:
            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.pixel_values 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,
            image_data_embedding_func=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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        if "InternLM2ForCausalLM" in self.config.llm_config.architectures:
            stacked_params_mapping = [
                # (param_name, shard_name, shard_id)
                ("gate_up_proj", "w1", 0),
                ("gate_up_proj", "w3", 1),
            ]
        elif "Qwen2ForCausalLM" in self.config.llm_config.architectures:
            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),
            ]
        params_dict = dict(self.named_parameters())

        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not 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:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                if "wqkv" in name:
                    config = self.config
                    kv_groups = config.num_attention_heads // config.num_key_value_heads
                    head_dim = config.hidden_size // config.num_attention_heads
                    loaded_weight = loaded_weight.view(
                        -1, 2 + kv_groups, head_dim, loaded_weight.shape[-1]
                    )
                    wq, wk, wv = torch.split(loaded_weight, [kv_groups, 1, 1], dim=1)
                    wq = wq.reshape(-1, wq.shape[-1])
                    wk = wk.reshape(-1, wk.shape[-1])
                    wv = wv.reshape(-1, wv.shape[-1])
                    weight_loader = param.weight_loader
                    weight_loader(param, wq, "q")
                    weight_loader(param, wk, "k")
                    weight_loader(param, wv, "v")
                else:
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)


EntryClass = InternVLChatModel