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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from typing import Optional,Tuple
import math
import logging
logger = logging.getLogger(__name__)
rwkv_emb_scale = 0.4 # try 0.4 for char-level english. try 1.0 for chinese.
rwkv_layer_decay = 1.0 # decay weights in higher layers. try 0.5 ~ 1.0.
class AttentionConfig:
def __init__(self, ctx_len=100, **kwargs):
self.ctx_len = ctx_len
for k,v in kwargs.items():
setattr(self, k, v)
########################################################################################################
# MHA_rotary: Multi-head Attention + Rotary Encoding + GeGLU FFN
########################################################################################################
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, base=10000):
super().__init__()
inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq)
self.seq_len_cached = None
self.cos_cached = None
self.sin_cached = None
def forward(self, x, seq_len=None):
if seq_len != self.seq_len_cached:
self.seq_len_cached = seq_len
t = torch.arange(seq_len, device=x.device)
freqs = torch.einsum('i,j->ij', t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self.cos_cached = emb.cos()
self.sin_cached = emb.sin()
return torch.stack([self.cos_cached, self.sin_cached])
class ContinuousRotaryEmbedding(torch.nn.Module):
'''Continuous rotary position embedding'''
def __init__(self, dim, sequence_scale):
super().__init__()
base=10000
self.sequence_scale = sequence_scale
self.register_buffer('inv_freq', 1. / (base ** (torch.arange(0, dim, 2))))
def forward(self, t):
t = (t + 0.5)* self.sequence_scale
freqs = torch.einsum('ij,k->ijk', t, self.inv_freq) # freqs: [B, L, dim//2]
emb = torch.cat((freqs, freqs), dim=-1).unsqueeze(1) # emb: [B, 1, L, dim], 1 for broadcast in head_num dim
return torch.stack([emb.cos(), emb.sin()])
def rotate_half(x):
x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), -1)
@torch.jit.script
def apply_rotary_pos_emb(q, k, cos, sin):
cos, sin = cos[...,:q.shape[2],:], sin[...,:q.shape[2],:]
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
class MHA_rotary(nn.Module):
def __init__(self, args):
super().__init__()
self.collect_attention_map = False
self.attention_map = None
assert args.encoder_dim % args.num_heads == 0
self.num_heads = args.num_heads
self.head_size = args.encoder_dim // args.num_heads
if args.timeshift:
self.time_shift = nn.ZeroPad2d((0,0,1,0))
self.query = nn.Linear(args.encoder_dim, args.encoder_dim)
self.key = nn.Linear(args.encoder_dim, args.encoder_dim)
self.value = nn.Linear(args.encoder_dim, args.encoder_dim)
# self.register_buffer("mask", torch.tril(torch.ones(config.ctx_len, config.ctx_len)))
self.rotary_ndims = int(self.head_size * 0.5)
self.rotary_emb = RotaryEmbedding(self.rotary_ndims)
self.output = nn.Linear(args.encoder_dim, args.encoder_dim)
def forward(self, x, RoPE, key_padding_mask=None):
B, T, C = x.size()
if hasattr(self, 'time_shift'):
x = torch.cat([self.time_shift(x)[:, :-1, :C//2], x[:, :, C//2:]], dim = -1)
q = self.query(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
k = self.key(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
v = self.value(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
q, query_pass = q[..., :self.rotary_ndims], q[..., self.rotary_ndims:]
k, key_pass = k[..., :self.rotary_ndims], k[..., self.rotary_ndims:]
# cos, sin = self.rotary_emb(q, seq_len=T)
cos, sin = RoPE
q, k = apply_rotary_pos_emb(q, k, cos, sin) # rotary encoding
q = torch.cat((q, query_pass), dim=-1)
k = torch.cat((k, key_pass), dim=-1)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) # self-attention: (B, nh, T, hs) * (B, nh, hs, T) -> (B, nh, T, T)
if key_padding_mask is not None:
key_padding_mask = key_padding_mask[:, None, None, :] # (B, T) -> (B, 1, 1, T)
att = att.masked_fill(key_padding_mask == 0, float('-inf'))
att = F.softmax(att, dim = -1) # softmax
x = att @ v # (B, nh, T, T) * (B, nh, T, hs) -> (B, nh, T, hs)
x = x.transpose(1, 2).contiguous().view(B, T, -1) # (B, nh, T, hs) -> (B, T, nh, hs) -> (B, T, C)
x = self.output(x)
if self.collect_attention_map:
self.attention_map = att
return x
class MHA_decoder(nn.Module):
def __init__(self, args):
super().__init__()
self.collect_attention_map = False
self.attention_map = None
assert args.encoder_dim % args.num_heads == 0
self.num_heads = args.num_heads
self.head_size = args.decoder_dim // args.num_heads
if args.timeshift:
self.time_shift = nn.ZeroPad2d((0,0,1,0))
self.query = nn.Linear(args.decoder_dim, args.decoder_dim)
self.key = nn.Linear(args.decoder_dim, args.decoder_dim)
self.value = nn.Linear(args.decoder_dim, args.decoder_dim)
# self.register_buffer("mask", torch.tril(torch.ones(config.ctx_len, config.ctx_len)))
self.rotary_ndims = int(self.head_size * 0.5)
self.rotary_emb = RotaryEmbedding(self.rotary_ndims)
self.output = nn.Linear(args.decoder_dim, args.decoder_dim)
def forward(self, x, memory,RoPE, key_padding_mask=None):
B, T, C = x.size()
_, L, M = memory.size()
# print("x size: ", x.size(), 'memory size: ', memory.size())
# print('B, T, C: ', B, T, C, 'L: ', L)
q = self.query(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
k = self.key(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
v = self.value(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
q, query_pass = q[..., :self.rotary_ndims], q[..., self.rotary_ndims:]
k, key_pass = k[..., :self.rotary_ndims], k[..., self.rotary_ndims:]
# cos, sin = self.rotary_emb(q, seq_len=T)
cos, sin = RoPE
q, k = apply_rotary_pos_emb(q, k, cos, sin) # rotary encoding
q = torch.cat((q, query_pass), dim=-1)
k = torch.cat((k, key_pass), dim=-1)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) # self-attention: (B, nh, T, hs) * (B, nh, hs, T) -> (B, nh, T, T)
if key_padding_mask is not None:
key_padding_mask = key_padding_mask[:, None, None, :] # (B, T) -> (B, 1, 1, T)
att = att.masked_fill(key_padding_mask == 0, float('-inf'))
att = F.softmax(att, dim = -1) # softmax
x = att @ v
# print("after attention vals: ", x.shape) # (B, nh, T, T) * (B, nh, T, hs) -> (B, nh, T, hs)
x = x.transpose(1, 2).contiguous().view(B, T, -1) # (B, nh, T, hs) -> (B, T, nh, hs) -> (B, T, C)
# x = self.output(x)
# print("after linear: ", x.shape) # (B, nh, T, T) * (B, nh, T, hs) -> (B, nh, T, hs)
# cross attention:
q = self.query(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
k = self.key(memory).view(B, L, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
v = self.value(memory).view(B, L, self.num_heads, self.head_size).transpose(1, 2) # (B, T, C) -> (B, nh, T, hs)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) # self-attention: (B, nh, T, hs) * (B, nh, hs, T) -> (B, nh, T, T)
# print("att size: ", att.size())
if key_padding_mask is not None:
key_padding_mask = key_padding_mask[:, None, None, :] # (B, T) -> (B, 1, 1, T)
att = att.masked_fill(key_padding_mask == 0, float('-inf'))
att = F.softmax(att, dim = -1) # softmax
x = att @ v # (B, nh, T, T) * (B, nh, T, hs) -> (B, nh, T, hs)
# print("x deocder size: ", x.size())
x = x.transpose(1, 2).contiguous().view(B, T, -1) # (B, nh, T, hs) -> (B, T, nh, hs) -> (B, T, C)
# print("x deocder size transposed: ", x.size())
x = self.output(x)
if self.collect_attention_map:
self.attention_map = att
return x
class GeGLU(torch.nn.Module):
def __init__(self, config, layer_id, time_shift = False):
super().__init__()
self.layer_id = layer_id
if time_shift:
self.time_shift = nn.ZeroPad2d((0,0,1,0))
hidden_sz = 3 * config.n_ffn
self.key = nn.Linear(config.n_embd, hidden_sz)
self.value = nn.Linear(config.n_embd, hidden_sz)
self.weight = nn.Linear(hidden_sz, config.n_embd)
def forward(self, x):
B, T, C = x.size()
if hasattr(self, 'time_shift'):
x = torch.cat([self.time_shift(x)[:, :-1, :C//2], x[:, :, C//2:]], dim = -1)
k = self.key(x)
v = self.value(x)
y = self.weight(F.gelu(k) * v)
return y |