aimv2.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# A modified implementation of the AIMv2 Transformer
# inserted here also the image tokenizer used by Ovis2
from collections.abc import Iterable
from typing import Optional

import torch
import torch.nn as nn
from transformers import PretrainedConfig

from vllm.attention.layer import MultiHeadAttention
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.distributed.utils import divide
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader


class AIMv2SwiGLUFFN(nn.Module):

    def __init__(self, config: PretrainedConfig,
                 quant_config: QuantizationConfig, prefix: str):
        super().__init__()
        hidden_features = config.intermediate_size
        in_features = config.hidden_size
        bias = config.use_bias

        self.fc13 = MergedColumnParallelLinear(
            in_features,
            [hidden_features] * 2,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.fc13",
        )
        self.fc2 = RowParallelLinear(
            input_size=hidden_features,
            output_size=in_features,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.fc2",
        )
        self.act_fn = SiluAndMul()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x, _ = self.fc13(x)
        x = self.act_fn(x)
        x, _ = self.fc2(x)
        return x


class AIMv2PatchEmbed(nn.Module):

    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.proj = nn.Conv2d(
            config.num_channels,
            config.hidden_size,
            kernel_size=(config.patch_size, config.patch_size),
            stride=(config.patch_size, config.patch_size),
        )
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm.forward_native(x)
        return x


class AIMv2ViTPreprocessor(nn.Module):

    def __init__(self, config: PretrainedConfig):
        super().__init__()
        num_patches = (config.image_size // config.patch_size)**2

        self.patchifier = AIMv2PatchEmbed(config)
        self.pos_embed = nn.Parameter(
            torch.zeros((1, num_patches, config.hidden_size)))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        tokens = self.patchifier(x)
        _, N, _ = tokens.shape
        pos_embed = self.pos_embed.to(tokens.device)
        tokens = tokens + pos_embed[:, :N]
        return tokens


class AIMv2Attention(nn.Module):

    def __init__(self, config: PretrainedConfig,
                 quant_config: QuantizationConfig, prefix: str):
        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
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                "embed_dim must be divisible by num_heads "
                f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads}).")
        self.scale = self.head_dim**-0.5

        self.qkv = QKVParallelLinear(
            hidden_size=self.embed_dim,
            head_size=self.head_dim,
            total_num_heads=self.num_heads,
            bias=config.qkv_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv",
        )

        self.proj = RowParallelLinear(
            input_size=self.embed_dim,
            output_size=self.embed_dim,
            bias=config.use_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.proj",
        )

        self.tp_size = get_tensor_model_parallel_world_size()
        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)

        self.attn = MultiHeadAttention(self.num_heads_per_partition,
                                       self.head_dim, self.scale)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        qkv, _ = self.qkv(x)
        q, k, v = qkv.chunk(3, dim=-1)

        x = self.attn(q, k, v)
        x, _ = self.proj(x)
        return x


class AIMv2Block(nn.Module):

    def __init__(self, config: PretrainedConfig,
                 quant_config: QuantizationConfig, prefix: str):
        super().__init__()
        self.attn = AIMv2Attention(config,
                                   quant_config=quant_config,
                                   prefix=f"{prefix}.attn")
        self.norm_1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.mlp = AIMv2SwiGLUFFN(config,
                                  quant_config=quant_config,
                                  prefix=f"{prefix}.mlp")
        self.norm_2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.norm_1.forward_native(x))
        x = x + self.mlp(self.norm_2.forward_native(x))
        return x


class AIMv2Transformer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: QuantizationConfig,
        *,
        require_post_norm: Optional[bool] = None,
        prefix: str = "",
    ):
        super().__init__()

        self.blocks = nn.ModuleList([
            AIMv2Block(config, quant_config, prefix=f"{prefix}.blocks.{i}")
            for i in range(config.num_hidden_layers)
        ])
        if require_post_norm:
            self.post_trunk_norm = RMSNorm(config.hidden_size,
                                           eps=config.rms_norm_eps)
        else:
            self.post_trunk_norm = None

    def forward(self, tokens: torch.Tensor) -> torch.Tensor:
        # they take the -1 as the ref embeddings, like a clip skip
        for block in self.blocks:
            tokens = block(tokens)
        if self.post_trunk_norm is not None:
            tokens = self.post_trunk_norm(tokens)
        return tokens


class AIMv2Model(torch.nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 quant_config: QuantizationConfig,
                 *,
                 require_post_norm: Optional[bool] = None,
                 prefix: str = ""):
        super().__init__()
        self.preprocessor = AIMv2ViTPreprocessor(config)
        self.trunk = AIMv2Transformer(config,
                                      quant_config=quant_config,
                                      require_post_norm=require_post_norm,
                                      prefix=f"{prefix}.trunk")

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:

        x = self.preprocessor(pixel_values)
        x = self.trunk(x)

        return x

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".fc13", ".fc1", 0),
            (".fc13", ".fc3", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()

        for name, loaded_weight in weights:
            # post_layernorm is optional in SiglipVisionModel
            if (name.startswith("trunk.post_trunk_norm")
                    and self.trunk.post_trunk_norm is None):
                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)

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params