File size: 8,119 Bytes
543135c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """HF-compatible inference wrapper for Slayer's trained BalancedLM weights."""
import math
import torch
from torch import nn
from torch.nn import functional as F
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
class SlayerBalancedConfig(PretrainedConfig):
model_type = "slayer_balanced"
def __init__(self, vocab_size=24576, hidden_size=576, intermediate_size=2496,
num_hidden_layers=26, num_attention_heads=9, num_key_value_heads=3,
head_dim=64, max_position_embeddings=2048, rms_norm_eps=1e-6,
rope_parameters=None, tie_word_embeddings=True, **kwargs):
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.max_position_embeddings = max_position_embeddings
self.rms_norm_eps = rms_norm_eps
self.rope_parameters = rope_parameters or {"rope_theta": 10000.0, "rope_type": "default"}
self.use_cache = False
class RMSNorm(nn.Module):
def __init__(self, width, eps):
super().__init__()
self.weight = nn.Parameter(torch.ones(width))
self.eps = eps
def forward(self, x):
dtype = x.dtype
x = x.float()
return self.weight * (x * torch.rsqrt(x.square().mean(-1, keepdim=True) + self.eps)).to(dtype)
class ValueProjection(nn.Linear):
def __init__(self, width, out, first):
super().__init__(width, out, bias=False)
if not first:
self.vr_lambda = nn.Parameter(torch.zeros(1))
class SlayerAttention(nn.Module):
def __init__(self, c, index):
super().__init__()
h = c.hidden_size
self.heads, self.kv, self.d = c.num_attention_heads, c.num_key_value_heads, c.head_dim
self.first = index == 0
self.q_proj = nn.Linear(h, self.heads * self.d, bias=False)
self.k_proj = nn.Linear(h, self.kv * self.d, bias=False)
self.v_proj = ValueProjection(h, self.kv * self.d, self.first)
self.o_proj = nn.Linear(self.heads * self.d, h, bias=False)
self.q_norm, self.k_norm = RMSNorm(self.d, c.rms_norm_eps), RMSNorm(self.d, c.rms_norm_eps)
def forward(self, x, cos, sin, v0, attention_mask=None, output_attentions=False):
b, t, _ = x.shape
q = self.q_norm(self.q_proj(x).view(b, t, self.heads, self.d)).transpose(1, 2)
k = self.k_norm(self.k_proj(x).view(b, t, self.kv, self.d)).transpose(1, 2)
v = self.v_proj(x)
if self.first:
v0 = v
else:
v = v + self.v_proj.vr_lambda * v0
v = v.view(b, t, self.kv, self.d).transpose(1, 2)
if self.heads != self.kv:
rep = self.heads // self.kv
k = k.repeat_interleave(rep, dim=1)
v = v.repeat_interleave(rep, dim=1)
def rotate(z):
return torch.cat((-z[..., self.d // 2:], z[..., :self.d // 2]), dim=-1)
q, k = q * cos + rotate(q) * sin, k * cos + rotate(k) * sin
probs = None
if output_attentions:
scores = (q @ k.transpose(-2, -1)) / math.sqrt(self.d)
causal = torch.ones((t, t), dtype=torch.bool, device=x.device).tril()
scores = scores.masked_fill(~causal, torch.finfo(scores.dtype).min)
if attention_mask is not None:
scores = scores.masked_fill(~attention_mask[:, None, None, :].bool(), torch.finfo(scores.dtype).min)
probs = scores.softmax(dim=-1)
out = probs @ v
else:
if attention_mask is None:
out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
else:
causal = torch.ones((t, t), dtype=torch.bool, device=x.device).tril()
allowed = causal[None, None] & attention_mask[:, None, None, :].bool()
out = F.scaled_dot_product_attention(q, k, v, attn_mask=allowed, is_causal=False)
out = self.o_proj(out.transpose(1, 2).contiguous().view(b, t, -1))
return out, v0, probs
class SlayerMLP(nn.Module):
def __init__(self, c):
super().__init__()
h, f = c.hidden_size, c.intermediate_size
self.gate_proj = nn.Linear(h, f, bias=False)
self.up_proj = nn.Linear(h, f, bias=False)
self.down_proj = nn.Linear(f, h, bias=False)
def forward(self, x):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class SlayerBlock(nn.Module):
def __init__(self, c, index):
super().__init__()
self.self_attn = SlayerAttention(c, index)
self.mlp = SlayerMLP(c)
self.input_layernorm = RMSNorm(c.hidden_size, c.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(c.hidden_size, c.rms_norm_eps)
def forward(self, x, cos, sin, v0, mask, output_attentions):
attn, v0, probs = self.self_attn(self.input_layernorm(x), cos, sin, v0, mask, output_attentions)
x = x + attn
return x + self.mlp(self.post_attention_layernorm(x)), v0, probs
class SlayerBackbone(nn.Module):
def __init__(self, c):
super().__init__()
self.embed_tokens = nn.Embedding(c.vocab_size, c.hidden_size)
self.layers = nn.ModuleList(SlayerBlock(c, i) for i in range(c.num_hidden_layers))
self.norm = RMSNorm(c.hidden_size, c.rms_norm_eps)
class SlayerForCausalLM(PreTrainedModel):
config_class = SlayerBalancedConfig
base_model_prefix = "model"
_no_split_modules = ["SlayerBlock"]
_supports_sdpa = True
def __init__(self, config):
super().__init__(config)
self.model = SlayerBackbone(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, value):
self.lm_head = value
def forward(self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, labels=None,
output_attentions=None, use_cache=None, return_dict=None, **kwargs):
if inputs_embeds is None:
inputs_embeds = self.model.embed_tokens(input_ids)
_, t, _ = inputs_embeds.shape
d = self.config.head_dim
theta = self.config.rope_parameters.get("rope_theta", 10000.0)
inv = 1.0 / theta ** (torch.arange(0, d, 2, device=inputs_embeds.device, dtype=torch.float32) / d)
if position_ids is None:
if attention_mask is None:
position_ids = torch.arange(t, device=inputs_embeds.device)[None, :].expand(inputs_embeds.shape[0], -1)
else:
position_ids = (attention_mask.long().cumsum(-1) - 1).clamp_min(0)
phase = position_ids.to(torch.float32).unsqueeze(-1) * inv
phase = torch.cat((phase, phase), dim=-1)
cos = phase.cos()[:, None].to(inputs_embeds.dtype)
sin = phase.sin()[:, None].to(inputs_embeds.dtype)
x, v0 = inputs_embeds, None
attentions = [] if output_attentions else None
for layer in self.model.layers:
x, v0, attn = layer(x, cos, sin, v0, attention_mask, bool(output_attentions))
if attentions is not None:
attentions.append(attn)
logits = self.lm_head(self.model.norm(x))
loss = None
if labels is not None:
loss = F.cross_entropy(logits[:, :-1].float().reshape(-1, logits.size(-1)), labels[:, 1:].reshape(-1))
return CausalLMOutputWithPast(
loss=loss, logits=logits, past_key_values=None,
attentions=tuple(attentions) if attentions is not None else None,
)
|