Update api.py

api.py

@@ -1,57 +1,56 @@
 from fastapi import FastAPI, Request
 from fastapi.responses import StreamingResponse
 import asyncio
-import json    
-import numpy as np    
-import tensorflow as tf    
-from tensorflow.keras import layers    
-import sentencepiece as spm    
-import requests  
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers
+import sentencepiece as spm
 
 app = FastAPI()
-  
-sp = spm.SentencePieceProcessor()  
-sp.load("kolig_unigram.model")  
-  
-pad_id = sp.piece_to_id("<pad>")  
-if pad_id == -1: pad_id = 0  
-start_id = sp.piece_to_id("<start>")  
-if start_id == -1: start_id = 1  
-end_id = sp.piece_to_id("< end >")  
-if end_id == -1: end_id = 2  
-unk_id = sp.piece_to_id("<unk>")  
-if unk_id == -1: unk_id = 3  
-  
-vocab_size = sp.get_piece_size()  
-max_len = 100  
-  
-def text_to_ids(text):  
-    return sp.encode(text, out_type=int)  
-  
-def ids_to_text(ids):  
-    return sp.decode(ids)  
-
-class RotaryPositionalEmbedding(layers.Layer):  
-    def __init__(self, dim):  
-        super().__init__()  
-        inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim))  
-        self.inv_freq = tf.constant(inv_freq, dtype=tf.float32)  
-  
-    def call(self, x):  
-        batch, heads, seq_len, depth = tf.unstack(tf.shape(x))  
-        t = tf.range(seq_len, dtype=tf.float32)  
-        freqs = tf.einsum('i,j->ij', t, self.inv_freq)  
-        emb_sin = tf.sin(freqs)  
-        emb_cos = tf.cos(freqs)  
-        emb_cos = tf.reshape(emb_cos, [1, 1, seq_len, -1])  
-        emb_sin = tf.reshape(emb_sin, [1, 1, seq_len, -1])  
-        x1 = x[..., ::2]  
-        x2 = x[..., 1::2]  
-        x_rotated = tf.stack([  
-            x1 * emb_cos - x2 * emb_sin,  
-            x1 * emb_sin + x2 * emb_cos  
-        ], axis=-1)  
-        x_rotated = tf.reshape(x_rotated, tf.shape(x))  
+
+# SentencePiece 로드
+sp = spm.SentencePieceProcessor()
+sp.load("kolig_unigram.model")
+
+pad_id = sp.piece_to_id("<pad>")
+if pad_id == -1: pad_id = 0
+start_id = sp.piece_to_id("<start>")
+if start_id == -1: start_id = 1
+end_id = sp.piece_to_id("<end>")
+if end_id == -1: end_id = 2
+unk_id = sp.piece_to_id("<unk>")
+if unk_id == -1: unk_id = 3
+
+vocab_size = sp.get_piece_size()
+max_len = 100
+
+def text_to_ids(text):
+    return sp.encode(text, out_type=int)
+
+def ids_to_text(ids):
+    return sp.decode(ids)
+
+class RotaryPositionalEmbedding(layers.Layer):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim))
+        self.inv_freq = tf.constant(inv_freq, dtype=tf.float32)
+
+    def call(self, x):
+        batch, heads, seq_len, depth = tf.unstack(tf.shape(x))
+        t = tf.range(seq_len, dtype=tf.float32)
+        freqs = tf.einsum('i,j->ij', t, self.inv_freq)
+        emb_sin = tf.sin(freqs)
+        emb_cos = tf.cos(freqs)
+        emb_cos = tf.reshape(emb_cos, [1, 1, seq_len, -1])
+        emb_sin = tf.reshape(emb_sin, [1, 1, seq_len, -1])
+        x1 = x[..., ::2]
+        x2 = x[..., 1::2]
+        x_rotated = tf.stack([
+            x1 * emb_cos - x2 * emb_sin,
+            x1 * emb_sin + x2 * emb_cos
+        ], axis=-1)
+        x_rotated = tf.reshape(x_rotated, tf.shape(x))
         return x_rotated
 
 class SwiGLU(tf.keras.layers.Layer):
@@ -64,77 +63,77 @@ class SwiGLU(tf.keras.layers.Layer):
         x_proj = self.proj(x)
         x_val, x_gate = tf.split(x_proj, 2, axis=-1)
         return self.out(x_val * tf.nn.silu(x_gate))
-        
+
 class GPTBlock(tf.keras.layers.Layer):
-    def __init__(self, d_model, d_ff, num_heads=8, dropout_rate=0.1, adapter_dim=64):  
-        super().__init__()  
-        self.ln1 = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
-        self.mha = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model // num_heads)  
-        self.dropout1 = tf.keras.layers.Dropout(dropout_rate) 
-        self.adapter_down = tf.keras.layers.Dense(adapter_dim, activation='gelu') 
-        self.adapter_up = tf.keras.layers.Dense(d_model)  
-  
-        self.ln2 = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
-        self.ffn = SwiGLU(d_model, d_ff)  
-        self.dropout2 = tf.keras.layers.Dropout(dropout_rate) 
-        self.rope = RotaryPositionalEmbedding(d_model // num_heads)  
-  
-    def call(self, x, training=False):  
-        x_norm = self.ln1(x)  
-        b, s, _ = tf.shape(x_norm)[0], tf.shape(x_norm)[1], tf.shape(x_norm)[2]  
-        h = self.mha.num_heads  
-        d = x_norm.shape[-1] // h  
-  
-        qkv = tf.reshape(x_norm, [b, s, h, d])  
-        qkv = tf.transpose(qkv, [0, 2, 1, 3])  
-        q = self.rope(qkv)  
-        k = self.rope(qkv)  
-        q = tf.reshape(tf.transpose(q, [0, 2, 1, 3]), [b, s, h * d])  
-        k = tf.reshape(tf.transpose(k, [0, 2, 1, 3]), [b, s, h * d])  
-  
-        attn_out = self.mha(query=q, value=x_norm, key=k, use_causal_mask=True, training=training)  
-        attn_out = self.dropout1(attn_out, training=training)  
+    def __init__(self, d_model, d_ff, num_heads=8, dropout_rate=0.1, adapter_dim=64):
+        super().__init__()
+        self.ln1 = tf.keras.layers.LayerNormalization(epsilon=1e-5)
+        self.mha = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model // num_heads)
+        self.dropout1 = tf.keras.layers.Dropout(dropout_rate)
+        self.adapter_down = tf.keras.layers.Dense(adapter_dim, activation='gelu')
+        self.adapter_up = tf.keras.layers.Dense(d_model)
+
+        self.ln2 = tf.keras.layers.LayerNormalization(epsilon=1e-5)
+        self.ffn = SwiGLU(d_model, d_ff)
+        self.dropout2 = tf.keras.layers.Dropout(dropout_rate)
+        self.rope = RotaryPositionalEmbedding(d_model // num_heads)
+
+    def call(self, x, training=False):
+        x_norm = self.ln1(x)
+        b, s, _ = tf.shape(x_norm)[0], tf.shape(x_norm)[1], tf.shape(x_norm)[2]
+        h = self.mha.num_heads
+        d = x_norm.shape[-1] // h
+
+        qkv = tf.reshape(x_norm, [b, s, h, d])
+        qkv = tf.transpose(qkv, [0, 2, 1, 3])
+        q = self.rope(qkv)
+        k = self.rope(qkv)
+        q = tf.reshape(tf.transpose(q, [0, 2, 1, 3]), [b, s, h * d])
+        k = tf.reshape(tf.transpose(k, [0, 2, 1, 3]), [b, s, h * d])
+
+        attn_out = self.mha(query=q, value=x_norm, key=k, use_causal_mask=True, training=training)
+        attn_out = self.dropout1(attn_out, training=training)
 
         adapter_out = self.adapter_up(self.adapter_down(attn_out))
-        attn_out = attn_out + adapter_out  
-  
-        x = x + attn_out  
-        ffn_out = self.ffn(self.ln2(x))  
-        x = x + self.dropout2(ffn_out, training=training)  
+        attn_out = attn_out + adapter_out
+
+        x = x + attn_out
+        ffn_out = self.ffn(self.ln2(x))
+        x = x + self.dropout2(ffn_out, training=training)
         return x
 
-class InteractGPT(tf.keras.Model):  
-    def __init__(self, vocab_size, seq_len, d_model, d_ff, n_layers, num_heads=8, dropout_rate=0.1):  
-        super().__init__()  
-        self.token_embedding = tf.keras.layers.Embedding(vocab_size, d_model)  
-        self.blocks = [GPTBlock(d_model, d_ff, num_heads, dropout_rate) for _ in range(n_layers)]  
-        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
-  
-    def call(self, x, training=False):  
-        x = self.token_embedding(x)  
-        for block in self.blocks:  
-            x = block(x, training=training)  
-        x = self.ln_f(x)  
-        logits = tf.matmul(x, self.token_embedding.embeddings, transpose_b=True)  
-        return logits  
-
-model = InteractGPT(vocab_size=vocab_size, seq_len=max_len, d_model=256, d_ff=1024, n_layers=6)    
-  
-dummy_input = tf.zeros((1, max_len), dtype=tf.int32)  # 배치1, 시퀀스길이 max_len  
-_ = model(dummy_input)  # 모델이 빌드됨  
-model.load_weights("InteractGPT.weights.h5")  
-print("모델 가중치 로드 완료!")  
-
-def generate_text_mirostat_top_p(model, prompt, max_len=100, max_gen=98,
-                                 temperature=1.0, min_len=20,
-                                 repetition_penalty=1.2, eta=0.1, m=100, p=0.9):
+class InteractGPT(tf.keras.Model):
+    def __init__(self, vocab_size, seq_len, d_model, d_ff, n_layers, num_heads=8, dropout_rate=0.1):
+        super().__init__()
+        self.token_embedding = tf.keras.layers.Embedding(vocab_size, d_model)
+        self.blocks = [GPTBlock(d_model, d_ff, num_heads, dropout_rate) for _ in range(n_layers)]
+        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=1e-5)
+
+    def call(self, x, training=False):
+        x = self.token_embedding(x)
+        for block in self.blocks:
+            x = block(x, training=training)
+        x = self.ln_f(x)
+        logits = tf.matmul(x, self.token_embedding.embeddings, transpose_b=True)
+        return logits
+
+model = InteractGPT(vocab_size=vocab_size, seq_len=max_len, d_model=256, d_ff=1024, n_layers=6)
+
+dummy_input = tf.zeros((1, max_len), dtype=tf.int32)
+_ = model(dummy_input)
+model.load_weights("InteractGPT.weights.h5")
+print("모델 가중치 로드 완료!")
+
+repetition_penalty = 1.2
+
+async def generate_text_stream(prompt: str):
     model_input = text_to_ids(f"<start> {prompt} <sep>")
     model_input = model_input[:max_len]
     generated = list(model_input)
 
-    tau = 5.0  # 초기 목표 surprise
+    tau = 5.0
 
-    for step in range(max_gen):
+    while True:
         pad_length = max(0, max_len - len(generated))
         input_padded = np.pad(generated, (0, pad_length), constant_values=pad_id)
         input_tensor = tf.convert_to_tensor([input_padded])
@@ -148,69 +147,35 @@ def generate_text_mirostat_top_p(model, prompt, max_len=100, max_gen=98,
         for token_id, count in token_counts.items():
             next_token_logits[token_id] /= (repetition_penalty ** count)
 
-        # 최소 길이 넘으면 종료 토큰 확률 낮추기
-        if len(generated) >= min_len:
+        if len(generated) >= 20:
             next_token_logits[end_id] -= 5.0
         next_token_logits[pad_id] -= 10.0
+        next_token_logits = next_token_logits / 1.0  # temperature 고정
 
-        # 온도 조절
-        next_token_logits = next_token_logits / temperature
-
-        # --- 미로스타트 + Top-p 샘플링 ---
         logits_stable = next_token_logits - np.max(next_token_logits)
         probs = np.exp(logits_stable)
         probs /= probs.sum()
 
-        # 1. mirostat top-m 후보 추리기
         sorted_indices = np.argsort(-probs)
-        top_indices = sorted_indices[:m]
+        top_indices = sorted_indices[:100]
         top_probs = probs[top_indices]
         top_probs /= top_probs.sum()
 
-        # 2. mirostat 샘플링
         sampled_index = np.random.choice(top_indices, p=top_probs)
-        sampled_prob = probs[sampled_index]
-        observed_surprise = -np.log(sampled_prob + 1e-9)
-        tau += eta * (observed_surprise - tau)
-
-        # 3. top-p 필터링
-        sorted_top_indices = top_indices[np.argsort(-top_probs)]
-        sorted_top_probs = np.sort(top_probs)[::-1]
-        cumulative_probs = np.cumsum(sorted_top_probs)
-        cutoff = np.searchsorted(cumulative_probs, p, side='left') + 1
-        filtered_indices = sorted_top_indices[:cutoff]
-        filtered_probs = sorted_top_probs[:cutoff]
-        filtered_probs /= filtered_probs.sum()
-
-        # 4. 최종 토큰 샘플링
-        final_token = np.random.choice(filtered_indices, p=filtered_probs)
-        generated.append(int(final_token))
-
-        decoded_text = sp.decode(generated)
-        # 특수 토큰 제거
-        for token in ["<start>", "<sep>", "<end>"]:
-            decoded_text = decoded_text.replace(token, "")
-
-        decoded_text = decoded_text.strip()
-
-        if len(generated) >= min_len and (final_token == end_id or decoded_text.endswith(('.', '!', '?'))):
-            yield decoded_text
-            break
-        
-async def async_generator_wrapper(prompt: str):
-    # 동기 제너레이터를 비동기로 감싸기
-    loop = asyncio.get_event_loop()
-    gen = generate_text_mirostat_top_p(model, prompt)
-    
-    for text_piece in gen:
-        yield text_piece
-        # 토큰 생성 속도 조절 (0.1초 딜레이)
+        generated.append(int(sampled_index))
+
+        new_token_text = sp.decode([int(sampled_index)])
+
+        # 특수 토큰 무시 및 종료 처리
+        if any(tok in new_token_text for tok in ["<start>", "<sep>", "<end>", "<pad>"]):
+            if sampled_index == end_id:
+                break
+            continue
+
+        yield new_token_text
         await asyncio.sleep(0.1)
 
 @app.get("/generate")
 async def generate(request: Request):
-    # 쿼리 파라미터로 prompt 받음, 없으면 기본값
     prompt = request.query_params.get("prompt", "안녕하세요")
-    
-    # 스트리밍 응답으로 보냄
-    return StreamingResponse(async_generator_wrapper(prompt), media_type="text/plain")
+    return StreamingResponse(generate_text_stream(prompt), media_type="text/plain")