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from typing import Union
import math
import torch
import torch.nn as nn
import re
from einops import rearrange, repeat
class IdentityMap(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, *args, **kwargs):
return x
@property
def config(self):
return {"mm_projector_type": 'identity'}
class SimpleResBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.pre_norm = nn.LayerNorm(channels)
self.proj = nn.Sequential(
nn.Linear(channels, channels),
nn.GELU(),
nn.Linear(channels, channels)
)
def forward(self, x):
x = self.pre_norm(x)
return x + self.proj(x)
class ResamplerBlock(nn.Module):
def __init__(
self,
hidden_size: int = 768,
image_hidden_size: int = 1024,
num_heads: int = 12,
intermediate_size: int = None
):
super().__init__()
assert hidden_size % num_heads == 0, "For MHSA, you must have number of heads divisible by initial hidden size"
intermediate_size = hidden_size * 4 if intermediate_size is None else intermediate_size
# intermediate_size = hidden_size * 4
self.scale = 1 / math.sqrt(hidden_size // num_heads)
self.num_heads = num_heads
self.to_q = nn.Linear(hidden_size, hidden_size, bias=False)
self.to_k = nn.Linear(image_hidden_size, hidden_size, bias=False)
self.to_v = nn.Linear(image_hidden_size, hidden_size, bias=False)
self.to_out = nn.Linear(hidden_size, hidden_size, bias=False)
self.feed_forward = nn.Sequential(
*[
nn.LayerNorm(hidden_size),
nn.Linear(hidden_size, intermediate_size, bias=False),
nn.GELU(),
nn.Linear(intermediate_size, hidden_size, bias=False),
]
)
# prenorm for image features
self.norm_image = nn.LayerNorm(image_hidden_size)
self.norm_hidden = nn.LayerNorm(hidden_size)
def forward(self, hidden_states: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
# prenorm
x = self.norm_image(x)
residual_hidden_states = hidden_states
hidden_states = self.norm_hidden(hidden_states)
# compute Q, K, V
queries = self.to_q(hidden_states)
keys = self.to_k(x)
values = self.to_v(x)
# rearrange them into multi-head format
queries = rearrange(queries, "b n (h d) -> b h n d", h=self.num_heads)
keys = rearrange(keys, "b n (h d) -> b h n d", h=self.num_heads)
values = rearrange(values, "b n (h d) -> b h n d", h=self.num_heads)
# rescale
queries = self.scale * queries
# compute QK^T
scores = torch.einsum("... i d, ... j d -> ... i j", queries, keys)
# for stability
scores = scores - scores.amax(dim=-1, keepdim=True).detach()
# softmax
attention_scores = scores.softmax(dim=-1) # b h i j (i: number of queries, j: number of keys)
# dot product with V
out = torch.einsum("... i j, ... j d -> ... i d", attention_scores, values)
out = rearrange(out, "b h n d -> b n (h d)", h=self.num_heads)
out = self.to_out(out) + residual_hidden_states
residual_out = out
out = self.feed_forward(out)
return out + residual_out
class Resampler(nn.Module):
def __init__(
self,
hidden_size: int = 768,
image_hidden_size: int = 1024,
final_hidden_size: int = 4096,
num_heads: int = 12,
intermediate_size: int = None,
num_queries: int = 128,
num_layers: int = 3,
initializer_range: float = 0.02
):
super().__init__()
self.resampler_blocks = nn.ModuleList(
[
ResamplerBlock(
hidden_size, image_hidden_size, num_heads, intermediate_size
) for _ in range(num_layers)
]
)
self.queries = nn.Parameter(torch.randn(num_queries, hidden_size))
self.post_norm = nn.LayerNorm(hidden_size)
self.final_proj = nn.Linear(hidden_size, final_hidden_size, bias=False)
# self.initializer_range = initializer_range
# for module in self.modules():
# if isinstance(module, (nn.Linear, nn.LayerNorm, nn.Conv2d)):
# self._init_weights(module)
#
# def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
# """Initialize the weights"""
# if isinstance(module, (nn.Linear, nn.Conv2d)):
# # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
# # `trunc_normal_cpu` not implemented in `half` issues
# module.weight.data = nn.init.trunc_normal_(
# module.weight.data.to(torch.float32), mean=0.0, std=self.initializer_range
# ).to(module.weight.dtype)
# if module.bias is not None:
# module.bias.data.zero_()
# elif isinstance(module, nn.LayerNorm):
# module.bias.data.zero_()
# module.weight.data.fill_(1.0)
def forward(self, image_hidden_states: torch.Tensor) -> torch.Tensor:
b = image_hidden_states.size(0)
queries = repeat(self.queries, 'n d -> b n d', b=b)
for resampler_block in self.resampler_blocks:
queries = resampler_block(queries, image_hidden_states)
# post norm
queries = self.post_norm(queries)
return self.final_proj(queries)
def build_vision_projector(config, delay_load=False, **kwargs):
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == 'linear':
return nn.Linear(config.mm_hidden_size, config.hidden_size)
if projector_type == 'resampler':
hidden_size = getattr(config, 'resampler_hidden_size', 768)
image_hidden_size = config.mm_hidden_size
num_queries = getattr(config, 'num_queries', 128)
final_hidden_size = config.hidden_size
num_heads = 12
if hidden_size == 512:
num_heads = 8
num_layers = getattr(config, 'num_resampler_layers', 3)
initializer_range = getattr(config, 'initializer_range', 0.02)
print(
f"resampler config: resampler hidden size: {hidden_size}, num_queries: {num_queries}, "
f"num_resampler_layers: {num_layers}"
)
return Resampler(
hidden_size=hidden_size,
image_hidden_size=image_hidden_size,
num_queries=num_queries,
final_hidden_size=final_hidden_size,
num_layers=num_layers,
num_heads=num_heads,
initializer_range=initializer_range
)
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
mlp = nn.Sequential(*modules)
if getattr(config, 'load_moe_mm_projector', False):
from deepspeed.moe.layer import MoE
mlp = MoE(
config.mm_hidden_size,
expert=mlp,
num_experts=4,
ep_size=1,
k=2,
capacity_factor=1.,
eval_capacity_factor=1.,
min_capacity=4,
use_residual=False,
)
def moe_forward_wrapper(forward_func):
return lambda *args, **kwargs: forward_func(*args, **kwargs)[0]
mlp.forward = moe_forward_wrapper(mlp.forward)
return mlp
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
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