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# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
from transformers.activations import ACT2FN
from transformers.utils import logging
from .configuration_navil_vit import NaViLVisionConfig
try:
# from .flash_attention import FlashAttention
from flash_attn import flash_attn_varlen_func
from flash_attn.layers.rotary import apply_rotary_emb
has_flash_attn = True
except:
print('FlashAttention is not installed.')
has_flash_attn = False
logger = logging.get_logger(__name__)
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)
try:
from apex.normalization import FusedRMSNorm
InternRMSNorm = FusedRMSNorm # noqa
logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
except ImportError:
# using the normal InternRMSNorm
pass
except Exception:
logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
pass
NORM2FN = {
'rms_norm': InternRMSNorm,
'layer_norm': nn.LayerNorm,
}
class InternVisionRotaryEmbedding(nn.Module):
def __init__(self, dim: int, theta: float = 10000.0) -> None:
super().__init__()
inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, seqlen: int) -> torch.Tensor:
seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
freqs = torch.outer(seq, self.inv_freq)
return freqs
class InternAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: NaViLVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.use_flash_attn = config.use_flash_attn and has_flash_attn
if config.use_flash_attn and not has_flash_attn:
print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
f' {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.attn_drop = nn.Dropout(config.attention_dropout)
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)
if self.use_flash_attn:
self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
self.proj = nn.Linear(self.embed_dim, self.embed_dim)
def _naive_attn(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
if self.qk_normalization:
B_, H_, N_, D_ = q.shape
q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
attn = ((q * self.scale) @ k.transpose(-2, -1))
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
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, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
)
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._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
return x
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
orig_dtype = tensor.dtype
tensor = tensor.float()
cos = freqs.cos()
sin = freqs.sin()
cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
output = (tensor * cos) + (rotate_half(tensor) * sin)
output = output.to(orig_dtype)
return output
class InternVisionSdpaAttention(nn.Module):
def __init__(self, config: NaViLVisionConfig) -> None:
super().__init__()
self.config = config
dim = config.hidden_size
num_heads = config.num_attention_heads
self.num_heads = num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=config.qkv_bias)
self.proj = nn.Linear(dim, dim)
self.qk_normalization = config.qk_normalization
if self.qk_normalization:
self.q_norm = InternRMSNorm(dim, eps=config.layer_norm_eps)
self.k_norm = InternRMSNorm(dim, eps=config.layer_norm_eps)
self.proj_drop = nn.Dropout(config.dropout)
def forward(
self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor = None
) -> torch.Tensor:
seq_length = hidden_states.shape[0]
q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
if self.qk_normalization:
q = self.q_norm(q.flatten(1).view(q.shape))
k = self.k_norm(k.flatten(1).view(k.shape))
q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool)
for i in range(1, len(cu_seqlens)):
attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True
q = q.transpose(0, 1)
k = k.transpose(0, 1)
v = v.transpose(0, 1)
attn_output = F.scaled_dot_product_attention(q, k, v, attention_mask, dropout_p=0.0)
attn_output = attn_output.transpose(0, 1)
attn_output = attn_output.reshape(seq_length, -1)
attn_output = self.proj(attn_output)
attn_output = self.proj_drop(attn_output)
return attn_output
def apply_rotary_pos_emb_flashatt(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
tensor_ = tensor.float()
cos = freqs.cos().float()
sin = freqs.sin().float()
output = apply_rotary_emb(tensor_, cos, sin).type_as(tensor)
return output
class InternVisionFlashAttention2(nn.Module):
def __init__(self, config: NaViLVisionConfig) -> None:
super().__init__()
self.config = config
dim = config.hidden_size
num_heads = config.num_attention_heads
self.num_heads = num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=config.qkv_bias)
self.proj = nn.Linear(dim, dim)
self.qk_normalization = config.qk_normalization
if self.qk_normalization:
self.q_norm = InternRMSNorm(dim, eps=config.layer_norm_eps)
self.k_norm = InternRMSNorm(dim, eps=config.layer_norm_eps)
self.proj_drop = nn.Dropout(config.dropout)
def forward(
self,
hidden_states: torch.Tensor,
cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor = None,
) -> torch.Tensor:
seq_length = hidden_states.shape[0]
q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
if self.qk_normalization:
q = self.q_norm(q.flatten(1).view(q.shape))
k = self.k_norm(k.flatten(1).view(k.shape))
q = apply_rotary_pos_emb_flashatt(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
k = apply_rotary_pos_emb_flashatt(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
attn_output = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape(
seq_length, -1
)
attn_output = self.proj(attn_output)
attn_output = self.proj_drop(attn_output)
return attn_output
class InternMLP(nn.Module):
def __init__(self, config: NaViLVisionConfig):
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
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