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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.cpp_extension import load
from typing import Dict, List, Optional, Tuple, Callable, Union
eps = torch.finfo(torch.float32).eps
def norm(x: torch.Tensor):
return torch.rms_norm(x, (x.size(-1),), eps=eps)
class Rotary(nn.Module):
def __init__(self, dim: int, max_seq_len: int):
super().__init__()
# half-truncate RoPE by @YouJiacheng (w/ base freq tuning)
angular_freq = (1 / 1024) ** torch.linspace(0, 1, steps=dim//4, dtype=torch.float32)
angular_freq = torch.cat([angular_freq, angular_freq.new_zeros(dim//4)])
t = torch.arange(max_seq_len, dtype=torch.float32)
theta = torch.einsum("i,j -> ij", t, angular_freq)
self.cos = nn.Buffer(theta.cos(), persistent=False)
self.sin = nn.Buffer(theta.sin(), persistent=False)
def forward(self, x_BTHD: torch.Tensor):
assert self.cos.size(0) >= x_BTHD.size(-3)
cos, sin = self.cos[None, :x_BTHD.size(-3), None, :], self.sin[None, :x_BTHD.size(-3), None, :]
x1, x2 = x_BTHD.to(dtype=torch.float32).chunk(2, dim=-1)
y1 = x1 * cos + x2 * sin
y2 = x1 * (-sin) + x2 * cos
return torch.cat((y1, y2), 3).type_as(x_BTHD)
class CausalSoftmaxAttention(nn.Module):
def __init__(
self,
layer_id: int,
layers: int,
num_heads: int,
vocab_size: int,
input_dims: int,
hidden_dims: Union[int, None] = None,
):
super().__init__()
self.layer_id = layer_id
self.head_dim = input_dims // num_heads
self.num_heads = num_heads
assert input_dims % self.num_heads == 0
H = self.num_heads
N = self.head_dim
C = input_dims
with torch.no_grad():
init_bounds = 0.5 / (C ** 0.5)
self.q_proj = nn.Linear(C, C, bias=False)
self.k_proj = nn.Linear(C, C, bias=False)
self.v_proj = nn.Linear(C, C, bias=False)
self.g_proj = nn.Linear(C, C, bias=False)
self.o_proj = nn.Linear(C, C, bias=False)
self.rotary = Rotary(N, 2048)
self.q_proj.weight.data.uniform_(-init_bounds, init_bounds)
self.k_proj.weight.data.uniform_(-init_bounds, init_bounds)
self.v_proj.weight.data.uniform_(-init_bounds, init_bounds)
self.g_proj.weight.data.uniform_(-init_bounds, init_bounds)
self.o_proj.weight.data.zero_()
def forward(self, x):
B, T, C = x.size()
H = self.num_heads
N = C // H
def forward1(x):
x = norm(x)
q = self.q_proj(x).view(B, T, H, N)
k = self.k_proj(x).view(B, T, H, N)
v = self.v_proj(x).view(B, T, H, N)
g = self.g_proj(x).sigmoid()
q, k = norm(q), norm(k)
q, k = self.rotary(q), self.rotary(k)
return (q, k, v, g)
(q, k, v, g) = torch.utils.checkpoint.checkpoint(forward1, x, use_reentrant=False)
x = F.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True).transpose(1, 2).contiguous().view(B, T, C)
x = self.o_proj(x * g)
return x
class MLP(nn.Module):
def __init__(
self,
layer_id: int,
layers: int,
num_heads: int,
vocab_size: int,
input_dims: int,
hidden_dims: Union[int, None] = None,
):
super().__init__()
self.layer_id = layer_id
C = input_dims
hidden_dims = hidden_dims or 4 * C
with torch.no_grad():
init_bounds = 0.5 / (C ** 0.5)
self.k_proj = nn.Linear(C, hidden_dims, bias=False)
self.v_proj = nn.Linear(hidden_dims, C, bias=False)
self.k_proj.weight.data.uniform_(-init_bounds, init_bounds)
self.v_proj.weight.data.zero_()
def forward(self, x):
B, T, C = x.size()
def forward1(x):
x = norm(x)
k = torch.relu(self.k_proj(x)).square()
return self.v_proj(k)
output = torch.utils.checkpoint.checkpoint(forward1, x, use_reentrant=False)
return output
class SoftmaxBlock(nn.Module):
def __init__(
self,
layer_id: int,
layers: int,
num_heads: int,
vocab_size: int,
input_dims: int,
hidden_dims: Union[int, None] = None,
):
super().__init__()
self.layer_id = layer_id
self.att = CausalSoftmaxAttention(layer_id, layers, num_heads, vocab_size, input_dims, hidden_dims)
self.ffn = MLP(layer_id, layers, num_heads, vocab_size, input_dims, hidden_dims)
def forward(self, x):
xx = self.att(x)
x = x + xx
xx = self.ffn(x)
x = x + xx
return x
class Transformer(nn.Module):
def __init__(
self,
layers: int,
num_heads: int,
vocab_size: int,
input_dims: int,
hidden_dims: Union[int, None] = None,
dtype = None
):
super().__init__()
self.emb = nn.Embedding(vocab_size, input_dims)
self.emb.weight.data.uniform_(-1e-4, 1e-4)
self.blocks = nn.ModuleList([SoftmaxBlock(i, layers, num_heads, vocab_size, input_dims, hidden_dims) for i in range(layers)])
def forward(self, idx):
x = norm(self.emb(idx))
for i, block in enumerate(self.blocks):
x = block(x)
x = norm(x)
logits = F.linear(x, self.emb.weight)
return logits |