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# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
from miche.michelangelo.models.modules.checkpoint import checkpoint
# Initialize linear layers with normal distribution weights and zero biases
def init_linear(l, stddev):
nn.init.normal_(l.weight, std=stddev)
if l.bias is not None:
nn.init.constant_(l.bias, 0.0)
# Multihead attention module
class MultiheadAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int, # Context size
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.heads = heads
self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadAttention(device=device, dtype=dtype, heads=heads, n_ctx=n_ctx, flash=flash)
init_linear(self.c_qkv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
x = self.c_qkv(x)
x = checkpoint(self.attention, (x,), (), True)
x = self.c_proj(x)
return x
# QKV multihead attention module
class QKVMultiheadAttention(nn.Module):
def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int, flash: bool = False):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_ctx = n_ctx
self.flash = flash
def forward(self, qkv):
bs, n_ctx, width = qkv.shape
attn_ch = width // self.heads // 3
scale = 1 / math.sqrt(math.sqrt(attn_ch))
qkv = qkv.view(bs, n_ctx, self.heads, -1)
q, k, v = torch.split(qkv, attn_ch, dim=-1)
if self.flash:
out = F.scaled_dot_product_attention(q, k, v)
else:
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
return out
# Residual attention block module
class ResidualAttentionBlock(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
use_checkpoint: bool = False,
n_ctx: int, # Context size
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.attn = MultiheadAttention(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
self.ln_2 = nn.LayerNorm(width, device=device, dtype=dtype)
def _forward(self, x: torch.Tensor):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
def forward(self, x: torch.Tensor):
return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)
# Multihead cross attention module
class MultiheadCrossAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_data: Optional[int] = None,
data_width: Optional[int] = None,
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
self.n_data = n_data
self.width = width
self.heads = heads
self.data_width = width if data_width is None else data_width
self.c_q = nn.Linear(width, width, bias=qkv_bias, device=device, dtype=dtype)
self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadCrossAttention(
device=device, dtype=dtype, heads=heads, n_data=n_data, flash=flash
)
init_linear(self.c_q, init_scale)
init_linear(self.c_kv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x, data):
x = self.c_q(x)
data = self.c_kv(data)
x = checkpoint(self.attention, (x, data), (), True)
x = self.c_proj(x)
return x
# QKV multihead cross attention module
class QKVMultiheadCrossAttention(nn.Module):
def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int,
flash: bool = False, n_data: Optional[int] = None):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_data = n_data
self.flash = flash
def forward(self, q, kv):
_, n_ctx, _ = q.shape
bs, n_data, width = kv.shape
attn_ch = width // self.heads // 2
scale = 1 / math.sqrt(math.sqrt(attn_ch))
q = q.view(bs, n_ctx, self.heads, -1)
kv = kv.view(bs, n_data, self.heads, -1)
k, v = torch.split(kv, attn_ch, dim=-1)
if self.flash:
out = F.scaled_dot_product_attention(q, k, v)
else:
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
return out
# Residual cross attention block module
class ResidualCrossAttentionBlock(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
n_data: Optional[int] = None,
data_width: Optional[int] = None,
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
if data_width is None:
data_width = width
self.attn = MultiheadCrossAttention(
device=device,
dtype=dtype,
n_data=n_data,
width=width,
heads=heads,
data_width=data_width,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.ln_2 = nn.LayerNorm(data_width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
self.ln_3 = nn.LayerNorm(width, device=device, dtype=dtype)
def forward(self, x: torch.Tensor, data: torch.Tensor):
x = x + self.attn(self.ln_1(x), self.ln_2(data))
x = x + self.mlp(self.ln_3(x))
return x
# MLP Module
class MLP(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
width: int,
init_scale: float):
super().__init__()
self.width = width
self.c_fc = nn.Linear(width, width * 4, device=device, dtype=dtype)
self.c_proj = nn.Linear(width * 4, width, device=device, dtype=dtype)
self.gelu = nn.GELU()
init_linear(self.c_fc, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
return self.c_proj(self.gelu(self.c_fc(x)))
# Transformer Module
class Transformer(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
layers: int,
use_checkpoint: bool = False,
n_ctx: int, # Context size
width: int, # Width of the input tensor
heads: int, # Number of attention heads
init_scale: float, # Initialization scale for weights
qkv_bias: bool, # Whether to use bias in QKV layers
flash: bool = False # Whether to use flash attention
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.layers = layers
self.resblocks = nn.ModuleList(
[
ResidualAttentionBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=use_checkpoint
)
for _ in range(layers)
]
)
def forward(self, x: torch.Tensor):
for block in self.resblocks:
x = block(x)
return x
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