Create modeling_dumbmath.py

modeling_dumbmath.py

@@ -0,0 +1,120 @@
+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}