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e8ac551 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | from __future__ import annotations
import math
from typing import cast
import torch
import torch.nn.functional as F
from torch import nn
from .configuration_hanse import HanseConfig
class HanseRMSNorm(nn.Module):
def __init__(self, hidden_size: int, eps: float) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
input_dtype = x.dtype
normalized = x.float() * torch.rsqrt(
x.float().pow(2).mean(dim=-1, keepdim=True) + self.eps
)
return normalized.to(input_dtype) * self.weight.to(input_dtype)
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
first, second = x.chunk(2, dim=-1)
return torch.cat((-second, first), dim=-1)
class RotaryEmbedding(nn.Module):
def __init__(self, head_dim: int, max_seq_len: int, theta: float) -> None:
super().__init__()
if head_dim % 2:
raise ValueError("RoPE benötigt eine gerade head_dim")
inverse_frequency = 1.0 / (
theta ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim)
)
positions = torch.arange(max_seq_len, dtype=torch.float32)
frequencies = torch.outer(positions, inverse_frequency)
angles = torch.cat((frequencies, frequencies), dim=-1)
self.cos_cached: torch.Tensor
self.sin_cached: torch.Tensor
self.register_buffer("cos_cached", angles.cos(), persistent=False)
self.register_buffer("sin_cached", angles.sin(), persistent=False)
def forward(
self, query: torch.Tensor, key: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
sequence_length = query.size(-2)
if sequence_length > self.cos_cached.size(0):
raise ValueError("Sequenz ist länger als max_seq_len")
cos = self.cos_cached[:sequence_length].to(dtype=query.dtype)[
None, None, :, :
]
sin = self.sin_cached[:sequence_length].to(dtype=query.dtype)[
None, None, :, :
]
return (
query * cos + _rotate_half(query) * sin,
key * cos + _rotate_half(key) * sin,
)
class SwiGLU(nn.Module):
def __init__(self, config: HanseConfig) -> None:
super().__init__()
self.gate_up = nn.Linear(
config.hidden_size, 2 * config.ffn_hidden_size, bias=False
)
self.down = nn.Linear(
config.ffn_hidden_size, config.hidden_size, bias=False
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
gate, value = self.gate_up(x).chunk(2, dim=-1)
return cast(torch.Tensor, self.down(F.silu(gate) * value))
class GroupedQueryAttention(nn.Module):
def __init__(self, config: HanseConfig) -> None:
super().__init__()
self.num_query_heads = config.num_query_heads
self.num_kv_heads = config.num_kv_heads
self.head_dim = config.head_dim
self.query = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
kv_size = config.num_kv_heads * config.head_dim
self.key = nn.Linear(config.hidden_size, kv_size, bias=False)
self.value = nn.Linear(config.hidden_size, kv_size, bias=False)
self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.query_norm = (
HanseRMSNorm(config.head_dim, config.norm_eps)
if config.qk_norm
else nn.Identity()
)
self.key_norm = (
HanseRMSNorm(config.head_dim, config.norm_eps)
if config.qk_norm
else nn.Identity()
)
self.rope = RotaryEmbedding(
config.head_dim, config.max_seq_len, config.rope_theta
)
def _split_heads(self, x: torch.Tensor, heads: int) -> torch.Tensor:
batch, sequence, _ = x.shape
return x.view(batch, sequence, heads, self.head_dim).transpose(1, 2)
def forward(self, x: torch.Tensor) -> torch.Tensor:
query = self._split_heads(self.query(x), self.num_query_heads)
key = self._split_heads(self.key(x), self.num_kv_heads)
value = self._split_heads(self.value(x), self.num_kv_heads)
query = self.query_norm(query)
key = self.key_norm(key)
query, key = self.rope(query, key)
groups = self.num_query_heads // self.num_kv_heads
key = key.repeat_interleave(groups, dim=1)
value = value.repeat_interleave(groups, dim=1)
attended = F.scaled_dot_product_attention(
query, key, value, is_causal=True
)
batch, _, sequence, _ = attended.shape
attended = attended.transpose(1, 2).reshape(
batch, sequence, self.num_query_heads * self.head_dim
)
return cast(torch.Tensor, self.output(attended))
class CausalConvMixer(nn.Module):
def __init__(self, config: HanseConfig) -> None:
super().__init__()
self.kernel_size = config.conv_kernel_size
self.input = nn.Linear(
config.hidden_size, 2 * config.hidden_size, bias=False
)
self.depthwise = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.conv_kernel_size,
groups=config.hidden_size,
bias=False,
)
self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
gate, value = self.input(x).chunk(2, dim=-1)
value = value.transpose(1, 2)
value = F.pad(value, (self.kernel_size - 1, 0))
value = self.depthwise(value).transpose(1, 2)
return cast(torch.Tensor, self.output(F.silu(gate) * value))
class HanseBlock(nn.Module):
def __init__(self, config: HanseConfig, kind: str) -> None:
super().__init__()
self.mixer_norm = HanseRMSNorm(config.hidden_size, config.norm_eps)
self.mixer: nn.Module
if kind == "A":
self.mixer = GroupedQueryAttention(config)
elif kind == "C":
self.mixer = CausalConvMixer(config)
else:
raise ValueError(f"Unbekannter Blocktyp: {kind}")
self.ffn_norm = HanseRMSNorm(config.hidden_size, config.norm_eps)
self.ffn = SwiGLU(config)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.mixer(self.mixer_norm(x))
return cast(torch.Tensor, x + self.ffn(self.ffn_norm(x)))
def initialize_weights(module: nn.Module, num_layers: int) -> None:
if isinstance(module, (nn.Linear, nn.Embedding, nn.Conv1d)):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if isinstance(module, (GroupedQueryAttention, CausalConvMixer)):
nn.init.normal_(
module.output.weight,
mean=0.0,
std=0.02 / math.sqrt(2 * num_layers),
)
elif isinstance(module, SwiGLU):
nn.init.normal_(
module.down.weight,
mean=0.0,
std=0.02 / math.sqrt(2 * num_layers),
)
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