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702689e | 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 | """TinyBuddy 100K — 84K parameter Llama-style model for Transformers."""
import math
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_tinybuddy import TinyBuddyConfig
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
def precompute_rope_cos_sin(head_dim, max_seq_len, theta=10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
t = torch.arange(max_seq_len, dtype=torch.float32)
freqs = torch.outer(t, freqs)
return freqs.cos(), freqs.sin()
def apply_rotary_emb(xq, xk, cos, sin):
*_, seq_len, head_dim = xq.shape
cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0)
sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)
cos = cos.repeat_interleave(2, dim=-1)
sin = sin.repeat_interleave(2, dim=-1)
def rotate(x):
x1, x2 = x[..., ::2], x[..., 1::2]
return x * cos + torch.cat([-x2, x1], dim=-1) * sin
return rotate(xq), rotate(xk)
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.n_heads = config.num_attention_heads
self.n_kv_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.n_heads
self.n_rep = self.n_heads // self.n_kv_heads
self.q_proj = nn.Linear(config.hidden_size, self.n_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(config.hidden_size, self.n_kv_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(config.hidden_size, self.n_kv_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.n_heads * self.head_dim, config.hidden_size, bias=False)
mask = torch.triu(torch.ones(config.block_size, config.block_size), diagonal=1).bool()
self.register_buffer("causal_mask", mask)
cos, sin = precompute_rope_cos_sin(self.head_dim, config.block_size, config.rope_theta)
self.register_buffer("rope_cos", cos)
self.register_buffer("rope_sin", sin)
def forward(self, x):
B, T, C = x.shape
q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
q, k = apply_rotary_emb(q, k, self.rope_cos, self.rope_sin)
if self.n_rep > 1:
k = k.repeat_interleave(self.n_rep, dim=1)
v = v.repeat_interleave(self.n_rep, dim=1)
att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
att = att.masked_fill(self.causal_mask[:T, :T], float("-inf"))
att = F.softmax(att, dim=-1)
y = (att @ v).transpose(1, 2).contiguous().view(B, T, C)
return self.o_proj(y)
class FeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
def forward(self, x):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class TransformerBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.attn_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
self.attn = CausalSelfAttention(config)
self.ffn_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
self.ffn = FeedForward(config)
def forward(self, x):
x = x + self.attn(self.attn_norm(x))
x = x + self.ffn(self.ffn_norm(x))
return x
class TinyBuddyForCausalLM(PreTrainedModel):
config_class = TinyBuddyConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
_no_split_modules = ["TransformerBlock"]
def __init__(self, config):
super().__init__(config)
self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([TransformerBlock(config) for _ in range(config.num_hidden_layers)])
self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.lm_head.weight = self.token_embedding.weight
self.post_init()
def _tie_weights(self):
if self.config.tie_word_embeddings:
self.lm_head.weight = self.token_embedding.weight
def _init_weights(self, module):
std = 0.02
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
def get_input_embeddings(self):
return self.token_embedding
def set_input_embeddings(self, value):
self.token_embedding = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **kwargs):
if past_key_values is not None:
input_ids = input_ids[:, -1:]
return {"input_ids": input_ids, "attention_mask": attention_mask}
def _reorder_cache(self, past_key_values, beam_idx):
return past_key_values
@property
def num_parameters(self):
return sum(p.numel() for p in self.parameters())
def forward(self, input_ids, attention_mask=None, labels=None, **kwargs):
x = self.token_embedding(input_ids)
for layer in self.layers:
x = layer(x)
x = self.norm(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = F.cross_entropy(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
return CausalLMOutputWithPast(loss=loss, logits=logits)
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