280 lines
11 KiB
Python
280 lines
11 KiB
Python
# adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_intern_vit.py
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# --------------------------------------------------------
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# InternVL
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# Copyright (c) 2023 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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from typing import Iterable, Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import PretrainedConfig
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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NORM2FN = {
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'rms_norm': RMSNorm,
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'layer_norm': nn.LayerNorm,
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}
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class InternVisionEmbeddings(nn.Module):
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def __init__(self, config: PretrainedConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))
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self.patch_embedding = nn.Conv2d(in_channels=3,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size)
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self.num_patches = (self.image_size // self.patch_size)**2
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self.num_positions = self.num_patches + 1
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self.position_embedding = nn.Parameter(
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torch.randn(1, self.num_positions, self.embed_dim))
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def _get_pos_embed(self, pos_embed, H, W):
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target_dtype = pos_embed.dtype
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pos_embed = pos_embed.float().reshape(
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1, self.image_size // self.patch_size,
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self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
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pos_embed = F.interpolate(pos_embed,
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size=(H, W),
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mode='bicubic',
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align_corners=False)
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pos_embed = pos_embed.reshape(1, -1, H * W).permute(0, 2,
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1).to(target_dtype)
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return pos_embed
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def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
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target_dtype = self.patch_embedding.weight.dtype
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patch_embeds = self.patch_embedding(pixel_values.to(
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target_dtype)) # shape = [*, channel, width, height]
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batch_size, _, height, width = patch_embeds.shape
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1,
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-1).to(target_dtype)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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position_embedding = torch.cat([
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self.position_embedding[:, :1, :],
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self._get_pos_embed(self.position_embedding[:, 1:, :], height,
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width)
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],
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dim=1)
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embeddings = embeddings + position_embedding.to(target_dtype)
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return embeddings
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class InternAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: PretrainedConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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f'embed_dim must be divisible by num_heads '
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f'(got `embed_dim`: {self.embed_dim} and `num_heads`:'
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f' {self.num_heads}).')
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self.scale = self.head_dim**-0.5
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self.qkv = nn.Linear(self.embed_dim,
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3 * self.embed_dim,
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bias=config.qkv_bias)
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self.qk_normalization = config.qk_normalization
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if self.qk_normalization:
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self.q_norm = RMSNorm(self.embed_dim, eps=config.layer_norm_eps)
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self.k_norm = RMSNorm(self.embed_dim, eps=config.layer_norm_eps)
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self.proj = nn.Linear(self.embed_dim, self.embed_dim)
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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
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C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0)
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if self.qk_normalization:
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B_, H_, N_, D_ = q.shape
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q = self.q_norm.forward_native(q.transpose(1, 2).flatten(
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-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
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k = self.k_norm.forward_native(k.transpose(1, 2).flatten(
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-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
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x = F.scaled_dot_product_attention(q, k, v, scale=self.scale)
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x = x.transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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return x
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class InternMLP(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.config = config
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self.activation_fn = get_act_fn(config.hidden_act)
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self.fc1 = ColumnParallelLinear(config.hidden_size,
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config.intermediate_size,
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bias=True,
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quant_config=quant_config)
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self.fc2 = RowParallelLinear(config.intermediate_size,
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config.hidden_size,
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bias=True,
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quant_config=quant_config)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.fc1(hidden_states)
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hidden_states = self.activation_fn(hidden_states)
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hidden_states, _ = self.fc2(hidden_states)
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return hidden_states
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class InternVisionEncoderLayer(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.embed_dim = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.norm_type = config.norm_type
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self.attn = InternAttention(config)
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self.mlp = InternMLP(config, quant_config=quant_config)
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self.norm1 = NORM2FN[self.norm_type](self.embed_dim,
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eps=config.layer_norm_eps)
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self.norm2 = NORM2FN[self.norm_type](self.embed_dim,
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eps=config.layer_norm_eps)
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self.ls1 = nn.Parameter(config.initializer_factor *
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torch.ones(self.embed_dim))
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self.ls2 = nn.Parameter(config.initializer_factor *
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torch.ones(self.embed_dim))
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def forward(
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self,
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hidden_states: torch.Tensor,
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):
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hidden_states = hidden_states + self.attn(
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self.norm1(hidden_states)) * self.ls1
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hidden_states = hidden_states + self.mlp(
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self.norm2(hidden_states)) * self.ls2
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return hidden_states
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class InternVisionEncoder(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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num_hidden_layers_override: Optional[int] = None):
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super().__init__()
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self.config = config
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if num_hidden_layers_override is None:
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num_hidden_layers = config.num_hidden_layers
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else:
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num_hidden_layers = num_hidden_layers_override
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self.layers = nn.ModuleList([
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InternVisionEncoderLayer(config=config, quant_config=quant_config)
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for _ in range(num_hidden_layers)
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])
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def forward(self, inputs_embeds: torch.Tensor):
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hidden_states = inputs_embeds
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for encoder_layer in self.layers:
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hidden_states = encoder_layer(hidden_states)
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return hidden_states
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class InternVisionModel(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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num_hidden_layers_override: Optional[int] = None):
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super().__init__()
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self.config = config
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self.embeddings = InternVisionEmbeddings(config)
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self.encoder = InternVisionEncoder(
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config=config,
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quant_config=quant_config,
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num_hidden_layers_override=num_hidden_layers_override)
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def resize_pos_embeddings(self, old_size, new_size, patch_size):
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pos_emb = self.embeddings.position_embedding
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_, num_positions, embed_dim = pos_emb.shape
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cls_emb = pos_emb[:, :1, :]
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pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size,
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old_size // patch_size,
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-1).permute(0, 3, 1, 2)
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pos_emb = F.interpolate(pos_emb.float(),
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size=new_size // patch_size,
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mode='bicubic',
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align_corners=False)
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pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim,
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-1).permute(0, 2, 1)
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pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
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self.embeddings.position_embedding = nn.Parameter(pos_emb)
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self.embeddings.image_size = new_size
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def get_input_embeddings(self):
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return self.embeddings
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def forward(
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self,
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pixel_values: Optional[torch.Tensor] = None,
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pixel_embeds: Optional[torch.Tensor] = None,
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) -> torch.FloatTensor:
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if pixel_values is None and pixel_embeds is None:
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raise ValueError(
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'You have to specify pixel_values or pixel_embeds')
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if pixel_embeds is not None:
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hidden_states = pixel_embeds
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elif pixel_values is not None:
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if pixel_values.ndim == 4:
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hidden_states = self.embeddings(pixel_values)
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else:
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raise ValueError(
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f'wrong pixel_values size: {pixel_values.shape}')
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encoder_outputs = self.encoder(inputs_embeds=hidden_states)
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return encoder_outputs
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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