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f9db966 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 | """HuggingFace-compatible model definition for TinyGPT2.
This file is self-contained so it works when downloaded from the HuggingFace Hub
with `trust_remote_code=True`.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
from configuration_tinygpt2 import TinyGPT2HFConfig
# ---------------------------------------------------------------------------
# Layers (self-contained copies so this file works standalone on HF Hub)
# ---------------------------------------------------------------------------
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):
rms = torch.sqrt(torch.mean(x ** 2, dim=-1, keepdim=True) + self.eps)
return self.weight * (x / rms)
def precompute_freqs_cis(dim, seq_len, theta=10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
t = torch.arange(seq_len, dtype=torch.float)
freqs = torch.outer(t, freqs)
return torch.polar(torch.ones_like(freqs), freqs)
def apply_rotary_emb(x, freqs_cis):
# x: (B, T, H, D)
x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
freqs_cis = freqs_cis[:x.shape[1]].view(1, x.shape[1], 1, -1)
x_rotated = x_complex * freqs_cis
return torch.view_as_real(x_rotated).flatten(-2).type_as(x)
class GroupedQueryAttention(nn.Module):
def __init__(self, n_embd, n_head, n_query_groups, dropout=0.1):
super().__init__()
assert n_head % n_query_groups == 0
self.n_head = n_head
self.n_query_groups = n_query_groups
self.head_dim = n_embd // n_head
self.q_proj = nn.Linear(n_embd, n_embd, bias=False)
self.k_proj = nn.Linear(n_embd, n_query_groups * self.head_dim, bias=False)
self.v_proj = nn.Linear(n_embd, n_query_groups * self.head_dim, bias=False)
self.out_proj = nn.Linear(n_embd, n_embd, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(self, x, freqs_cis, is_causal=True, kv_cache=None):
B, T, C = x.shape
H, G, D = self.n_head, self.n_query_groups, self.head_dim
q = self.q_proj(x).view(B, T, H, D)
k = self.k_proj(x).view(B, T, G, D)
v = self.v_proj(x).view(B, T, G, D)
q = apply_rotary_emb(q, freqs_cis)
k = apply_rotary_emb(k, freqs_cis)
if kv_cache is not None:
k_past, v_past = kv_cache
k = torch.cat([k_past, k], dim=1)
v = torch.cat([v_past, v], dim=1)
new_kv_cache = (k, v)
k = k[:, :, :, None, :].expand(B, -1, G, H // G, D).reshape(B, -1, H, D)
v = v[:, :, :, None, :].expand(B, -1, G, H // G, D).reshape(B, -1, H, D)
q, k, v = (t.transpose(1, 2) for t in (q, k, v))
use_causal = is_causal and kv_cache is None
attn_output = F.scaled_dot_product_attention(q, k, v, is_causal=use_causal)
attn_output = attn_output.transpose(1, 2).contiguous().view(B, T, C)
return self.out_proj(attn_output), new_kv_cache
class TinyGPT2Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln1 = RMSNorm(config.n_embd)
self.attn = GroupedQueryAttention(
config.n_embd, config.n_head, config.gqa_kv_head, config.dropout
)
self.ln2 = RMSNorm(config.n_embd)
self.ffwd = nn.Sequential(
nn.Linear(config.n_embd, config.hidden_size),
nn.GELU(),
nn.Linear(config.hidden_size, config.n_embd),
nn.Dropout(config.dropout),
)
def forward(self, x, freqs_cis, is_causal=True, kv_cache=None):
residual = x
x = self.ln1(x)
attn_out, new_kv_cache = self.attn(x, freqs_cis, is_causal, kv_cache)
x = residual + attn_out
residual = x
x = self.ln2(x)
x = residual + self.ffwd(x)
return x, new_kv_cache
# ---------------------------------------------------------------------------
# HuggingFace PreTrainedModel wrapper
# ---------------------------------------------------------------------------
class TinyGPT2ForCausalLM(PreTrainedModel, GenerationMixin):
_tied_weights_keys = {"lm_head.weight": "token_embedding.weight"}
config_class = TinyGPT2HFConfig
def __init__(self, config: TinyGPT2HFConfig):
super().__init__(config)
self.config = config
self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd)
self.blocks = nn.ModuleList(
[TinyGPT2Block(config) for _ in range(config.n_layer)]
)
self.ln_f = RMSNorm(config.n_embd)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# Weight tying
self.token_embedding.weight = self.lm_head.weight
# Precompute RoPE frequencies
self.register_buffer(
"freqs_cis",
precompute_freqs_cis(
config.n_embd // config.n_head, config.block_size * 2
),
)
self.post_init()
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 forward(
self,
input_ids=None,
attention_mask=None,
past_key_values=None,
labels=None,
use_cache=False,
**kwargs,
):
B, T = input_ids.shape
x = self.token_embedding(input_ids)
if past_key_values is not None and len(past_key_values) > 0:
start_pos = past_key_values[0][0].shape[1] # length of cached keys
freqs_cis = self.freqs_cis[start_pos : start_pos + T]
else:
freqs_cis = self.freqs_cis[:T]
new_kv_caches = []
for i, block in enumerate(self.blocks):
kv_cache = past_key_values[i] if past_key_values else None
x, new_cache = block(x, freqs_cis, is_causal=True, kv_cache=kv_cache)
new_kv_caches.append(new_cache)
x = self.ln_f(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, shift_logits.size(-1)),
shift_labels.view(-1),
ignore_index=self.config.pad_token_id,
)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=new_kv_caches if use_cache else None,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
if past_key_values is not None and len(past_key_values) > 0:
input_ids = input_ids[:, -1:]
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"use_cache": True,
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
return tuple(
(k.index_select(0, beam_idx), v.index_select(0, beam_idx))
for k, v in past_key_values
)
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