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02420d2 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_dumbmath import DumbMathConfig
# ---------------------------------------------------------
# あなたのコンポーネント定義 (そのまま使用)
# ---------------------------------------------------------
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):
variance = x.pow(2).mean(-1, keepdim=True)
return x * torch.rsqrt(variance + self.eps) * self.weight
class SwiGLUMLP(nn.Module):
def __init__(self, d_model, d_ff):
super().__init__()
self.w_gate = nn.Linear(d_model, d_ff, bias=False)
self.w_up = nn.Linear(d_model, d_ff, bias=False)
self.w_down = nn.Linear(d_ff, d_model, bias=False)
def forward(self, x):
return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads):
super().__init__()
self.n_heads = n_heads
self.d_model = d_model
self.head_dim = d_model // n_heads
self.q_proj = nn.Linear(d_model, d_model, bias=False)
self.k_proj = nn.Linear(d_model, d_model, bias=False)
self.v_proj = nn.Linear(d_model, d_model, bias=False)
self.out_proj = nn.Linear(d_model, d_model, bias=False)
def forward(self, x):
b, t, c = x.size()
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_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(b, t, self.n_heads, self.head_dim).transpose(1, 2)
attn_out = F.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True)
attn_out = attn_out.transpose(1, 2).contiguous().view(b, t, c)
return self.out_proj(attn_out)
class DecoderBlock(nn.Module):
def __init__(self, d_model, n_heads, d_ff):
super().__init__()
self.attn_norm = RMSNorm(d_model)
self.attn = MultiHeadAttention(d_model, n_heads)
self.ffn_norm = RMSNorm(d_model)
self.ffn = SwiGLUMLP(d_model, d_ff)
def forward(self, x):
x = x + self.attn(self.attn_norm(x))
x = x + self.ffn(self.ffn_norm(x))
return x
# ---------------------------------------------------------
# Hugging Face規格のラッパークラス
# ---------------------------------------------------------
class DumbMathForCausalLM(PreTrainedModel):
config_class = DumbMathConfig
base_model_prefix = "model"
def __init__(self, config):
super().__init__(config)
self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)
self.pos_embedding = nn.Parameter(torch.zeros(1, config.max_len, config.d_model))
self.layers = nn.ModuleList([
DecoderBlock(config.d_model, config.n_heads, config.d_ff) for _ in range(config.n_layers)
])
self.ln_f = RMSNorm(config.d_model)
self.head = nn.Linear(config.d_model, config.vocab_size, bias=False)
# 重み初期化の適用
self.post_init()
def get_input_embeddings(self):
return self.token_embedding
def set_input_embeddings(self, value):
self.token_embedding = value
def forward(self, input_ids, labels=None, attention_mask=None, **kwargs):
b, t = input_ids.size()
x = self.token_embedding(input_ids) + self.pos_embedding[:, :t, :]
for layer in self.layers:
x = layer(x)
x = self.ln_f(x)
logits = self.head(x)
# ロス計算（Trainerに対応）
loss = None
if labels is not None:
# 標準的なCausal LMのロス計算（右シフト処理はTrainerが自動実行またはここで対応）
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
return CausalLMOutputWithPast(
loss=loss,
logits=logits
)
# 推論時(model.generate)に必要なメソッド定義
def prepare_inputs_for_generation(self, input_ids, **kwargs):
return {"input_ids": input_ids} |