Model.py

!pip install sentencepiece

import sentencepiece as spm

import os, json, numpy as np, tensorflow as tf

from tensorflow.keras import layers, Model

import requests

from tensorflow import keras

from tensorflow.keras import layers

import tensorflow.keras.backend as K



print('1')



tf.get_logger().setLevel("ERROR")

SEED = 42

tf.random.set_seed(SEED)

np.random.seed(SEED)



# TPU 초기화

try:
    resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu="local")
    tf.tpu.experimental.initialize_tpu_system(resolver)
    strategy = tf.distribute.TPUStrategy(resolver)
    print("✅ TPU 초기화 완료:", resolver.cluster_spec().as_dict())
    on_tpu = True

except Exception as e:
    print("⚠️ TPU 미사용, GPU/CPU로 진행:", e)
    strategy = tf.distribute.get_strategy()
    on_tpu = False

# Mixed precision
from tensorflow.keras import mixed_precision
policy = mixed_precision.Policy("mixed_bfloat16" if on_tpu else "float32")
mixed_precision.set_global_policy(policy)
print("✅ Mixed precision:", policy)

# =======================
# 1) 파일 다운로드
# =======================

def download_file(url, save_path):
    r = requests.get(url, stream=True)
    r.raise_for_status()
    with open(save_path, "wb") as f:
        for chunk in r.iter_content(8192*2):
            f.write(chunk)
    print(f"✅ {save_path} 저장됨")

DATA_PATH = "converted.jsonl"
TOKENIZER_PATH = "ko_unigram.model"

if not os.path.exists(DATA_PATH):
    download_file(
        "https://huggingface.co/datasets/Yuchan5386/SFT/resolve/main/data_shuffled_1.jsonl?download=true",
        DATA_PATH
    )

if not os.path.exists(TOKENIZER_PATH):
    download_file(
        "https://huggingface.co/Yuchan5386/inlam-100m/resolve/main/ko_unigram.model?download=true",
        TOKENIZER_PATH
    )

sp = spm.SentencePieceProcessor(TOKENIZER_PATH)

pad_id = sp.piece_to_id("<pad>") if sp.piece_to_id("<pad>") != -1 else 0
start_id = sp.piece_to_id("<start>")
sep_id = sp.piece_to_id("<sep>")
end_id = sp.piece_to_id("<end>")
unk_id = sp.piece_to_id("<unk>")
vocab_size = sp.get_piece_size()
print(f"✅ Vocabulary size: {vocab_size}")

max_len = 200
batch_size = 128

def text_to_ids(text):
    return sp.encode(text, out_type=int)

def ids_to_text(ids):
    return sp.decode(ids)


def jsonl_stream(file_path):
    with open(file_path, "r", encoding="utf-8") as f:
        for line in f:
            data = json.loads(line)
            conversations = data.get("conversations", [])
            for i in range(0, len(conversations) - 1, 2):
                human_msg = conversations[i]
                gpt_msg   = conversations[i + 1]
                if human_msg.get("from") != "human" or gpt_msg.get("from") != "gpt":
                    continue
                    
                prompt   = human_msg.get("value", "").strip()
                response = gpt_msg.get("value", "").strip()
                full = f"<start> {prompt} <sep> {response} <end>"
                if "<sep>" not in full:
                    continue

                sep_index  = full.index("<sep>")
                input_text = full[:sep_index + len("<sep>")].strip()
                target_text = full[sep_index + len("<sep>"):].strip()
                input_ids  = text_to_ids(input_text)
                target_ids = text_to_ids(target_text + " <end>")
                available_len = max_len - len(input_ids)

                if available_len <= 0:
                    input_ids = input_ids[-max_len:]
                    target_ids = []
                    target_mask = [0] * len(input_ids)
                else:
                    target_ids = target_ids[:available_len]
                    target_mask = [0] * len(input_ids) + [1] * len(target_ids)

                full_input = input_ids + target_ids
                pad_len = max_len - len(full_input)
                full_input += [pad_id] * pad_len
                target_mask += [0] * pad_len
                target_seq = full_input[1:] + [end_id]
                target_seq = target_seq[:max_len]
                masked_target = [
                    t if m == 1 else pad_id
                    for t, m in zip(target_seq, target_mask)
                ]
                yield (
                    tf.convert_to_tensor(full_input, dtype=tf.int32),
                    tf.convert_to_tensor(masked_target, dtype=tf.int32)
                )

dataset = tf.data.Dataset.from_generator(
    lambda: jsonl_stream(DATA_PATH),
    output_signature=(
        tf.TensorSpec(shape=(max_len,), dtype=tf.int32),
        tf.TensorSpec(shape=(max_len,), dtype=tf.int32),
    ),
)

dataset = dataset.shuffle(1000, seed=SEED).batch(batch_size, drop_remainder=True).prefetch(tf.data.AUTOTUNE)

with strategy.scope():
    dist_dataset = strategy.experimental_distribute_dataset(dataset)


class Lo(layers.Layer):
    def __init__(self, d_model):
        super().__init__()
        # 내부 계산은 float32로 유지
        self.proj = layers.Dense(d_model, use_bias=True, dtype='float32')
        self.p = layers.Dense(96, use_bias=True, dtype='float32')
        self._out_dtype = 'float32'

    def call(self, x):
        # x may be bfloat16; cast to float32 for stable intermediate computation
        x_f32 = tf.cast(x, tf.float32)
        x = self.proj(x_f32)
        x = tf.nn.gelu(x)
        x = self.p(x)
        # cast back to model dtype for consistency
        return tf.cast(x, self._out_dtype)

class LoSoU(layers.Layer):
    """
    안정화된 LoSoU 레이어 (동적 alpha 사용)
    - alpha 값을 입력에 따라 동적으로 계산: alpha = sigmoid(Linear(x))
    - 누적합 대신 지수이동평균(EMA) 사용 (alpha: smoothing factor)
    - 내부 계산은 float32로 수행 (TPU bfloat16 안정성 향상)
    - EMA 결과 클리핑 및 작은 epsilon 적용
    - 안전한 split 처리 (짝수 차원 가정; 아니라면 마지막 차원 pad 필요)
    """
    def __init__(self, d_model, clip_value=5.0, eps=1e-6):
        super().__init__()
        # 대부분 연산을 float32로 수행
        self.d_model = d_model
        self.clip_value = float(clip_value)
        self.eps = float(eps)

        # projection / gating layers in float32
        self.Q = layers.Dense(96, dtype='float32')
        self.K = layers.Dense(96, dtype='float32')
        self.V = Lo(d_model)  # Lo already handles casting to model dtype; we'll cast back to float32
        self.proj = layers.Dense(d_model, use_bias=True, dtype='float32')
        self.O = layers.Dense(d_model, dtype='float32')
        self.norm = layers.LayerNormalization(epsilon=1e-5, dtype='float32')

        # 동적 alpha 계산을 위한 레이어
        # alpha는 [0, 1] 범위여야 하므로 sigmoid 사용
        # 입력 x의 d_model 차원을 사용하여 각 샘플에 대해 alpha 계산
        # 예: (B, L, d_model) -> (B, L, 1) -> (B, L, 1) with sigmoid
        # 또는 (B, L, d_model) -> (B, L, d_model) -> global reduce -> (B, L, 1)
        # 간단히 각 위치에 대해 동일한 alpha 사용 (입력의 평균 기반)
        # 또는 위치별로 다르게 사용 (각 위치에 대해 계산)
        # 여기서는 위치별로 다르게 계산 (B, L, 1)
        self.alpha_linear = layers.Dense(1, activation='sigmoid', dtype='float32')

    def _ema_over_time(self, score, alpha_dynamic):
        # score: (B, L, D) float32 in [0,1] roughly
        # alpha_dynamic: (B, L, 1) float32 in [0,1]

        # transpose to (L, B, D) to scan over time steps
        seq = tf.transpose(score, perm=[1, 0, 2])  # (L, B, D)
        alpha_seq = tf.transpose(alpha_dynamic, perm=[1, 0, 2])  # (L, B, 1)

        def step(prev_ema, inputs):
            x_t, alpha_t = inputs
            # prev_ema: (B, D), x_t: (B, D), alpha_t: (B, 1)
            new = alpha_t * x_t + (1.0 - alpha_t) * prev_ema
            return new

        # 초기값을 첫 step 값으로 설정
        init = seq[0]  # (B, D)
        first_alpha = alpha_seq[0]  # (B, 1)

        # scan의 elems는 (L-1, B, D) 및 (L-1, B, 1) 이어야 함
        remaining_seq = seq[1:]  # (L-1, B, D)
        remaining_alpha = alpha_seq[1:]  # (L-1, B, 1)

        # elems는 두 텐서의 튜플로 구성: (x_t, alpha_t)
        elems = (remaining_seq, remaining_alpha)

        ema_seq = tf.scan(fn=step, elems=elems, initializer=init)
        # 초기값 포함
        ema_seq = tf.concat([tf.expand_dims(init, 0), ema_seq], axis=0)  # (L, B, D)

        # transpose back to (B, L, D)
        ema = tf.transpose(ema_seq, perm=[1, 0, 2])
        return ema

    def call(self, x):
        # x: (B, L, d_model) maybe bfloat16 or float32
        # cast to float32 for all internal computations
        x_f32 = tf.cast(x, tf.float32)
        residual = x_f32

        # Q, K, V
        q = self.Q(x_f32)   # (B, L, 96)
        k = self.K(x_f32)   # (B, L, 96)
        V = tf.cast(self.V(x), tf.float32)  # ensure V's output is float32

        # gating signals in (0,1)
        g_q = tf.nn.sigmoid(q)
        g_k = tf.nn.sigmoid(k)

        # elementwise product -> bounded roughly [0,1]
        score = g_q * g_k

        # 동적 alpha 계산: (B, L, d_model) -> (B, L, 1)
        alpha_dynamic = self.alpha_linear(x_f32) * 0.8 + 0.1 # (B, L, 1)
        # 필요시 alpha_dynamic에 대한 후처리 (예: min/max 등) 가능
        # ex: alpha_dynamic = tf.clip_by_value(alpha_dynamic, 0.01, 0.99)

        # EMA across time (stable alternative to cumsum)
        score_ema = self._ema_over_time(score, alpha_dynamic)

        # optionally normalize by (mean + eps) across last dim to reduce scale variations
        mean_last = tf.reduce_mean(score_ema, axis=-1, keepdims=True)  # (B, L, 1)
        denom = tf.maximum(mean_last, self.eps)
        score_norm = score_ema / denom

        # clip to avoid extremes
        score_clipped = tf.clip_by_value(score_norm, -self.clip_value, self.clip_value)

        # combine with V
        x_comb = score_clipped * V  # (B, L, d_model)

        out = self.proj(x_comb)  # (B, L, d_model)

        # ensure out dim even for split
        d = out.shape[-1]  # this is an int (static shape)
        if d is not None and d % 2 == 1:
            out = tf.pad(out, [[0,0],[0,0],[0,1]])

        a, b = tf.split(out, 2, axis=-1)
        gated = tf.nn.silu(a) * b
        out = self.O(gated)

        out = self.norm(out + residual)

        # cast back to original dtype for downstream layers
        return tf.cast(out, x.dtype)

class Block(layers.Layer):
    def __init__(self, d_model, hyper_n):
        super().__init__()
        self.losou = [LoSoU(d_model) for _ in range(hyper_n)]

    def call(self, x):
        for losou in self.losou:
            x = losou(x)
        return x

class ReLaM(tf.keras.Model):
    def __init__(self, vocab_size, max_seq_len, d_model, n_layers, dropout_rate=0.1):
        super().__init__()
        self.token_embedding = layers.Embedding(vocab_size, 128)
        self.pos_embedding = layers.Embedding(max_seq_len, d_model)
        self.blocks = [Block(d_model, hyper_n=1) for _ in range(n_layers)]

        # LayerNormalization은 float32로 해서 정밀도 문제 방지
        self.ln_f = layers.LayerNormalization(epsilon=1e-5, dtype="float32")

    def call(self, x, training=False):
        batch_size, seq_len = tf.shape(x)[0], tf.shape(x)[1]
        positions = tf.range(seq_len)[tf.newaxis, :]

        x = self.token_embedding(x) + self.pos_embedding(positions)
        for block in self.blocks:
            x = block(x)

        x = self.ln_f(x)

        embedding_matrix = tf.cast(self.token_embedding.embeddings, x.dtype)
        logits = tf.matmul(x, embedding_matrix, transpose_b=True)
        return tf.cast(logits, tf.float32)

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')

def masked_loss(y_true, y_pred):
    loss = loss_fn(y_true, y_pred)
    mask = tf.cast(tf.not_equal(y_true, pad_id), tf.float32)
    masked_loss = tf.reduce_sum(loss * mask) / tf.reduce_sum(mask)
    return masked_loss

def masked_perplexity(y_true, y_pred):
    loss = loss_fn(y_true, y_pred)
    mask = tf.cast(tf.not_equal(y_true, pad_id), tf.float32)
    avg_loss = tf.reduce_sum(loss * mask) / tf.reduce_sum(mask)
    return tf.exp(tf.minimum(avg_loss, 10.0))  # 수치 안정성 확보

def create_lr_schedule(initial_lr=5e-5, decay_steps=10000, decay_rate=0.9):
    return tf.keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate=initial_lr,
        decay_steps=decay_steps,
        decay_rate=decay_rate,
        staircase=False
    )

# 모델 생성
model = ReLaM(
    vocab_size=vocab_size,
    max_seq_len=max_len,
    d_model=256,
    n_layers=1
)

# 옵티마이저 설정
optimizer = tf.keras.optimizers.Adam(
    learning_rate=create_lr_schedule(),
    beta_1=0.9,
    beta_2=0.95,
    epsilon=1e-8,
    clipnorm=1.0
)

# 모델 컴파일
model.compile(
    optimizer=optimizer,
    loss=masked_loss,
    metrics=[
        masked_perplexity
    ]
)

# 더미 인풋으로 모델 초기화
dummy_input = np.zeros((1, max_len), dtype=np.int32)
model(dummy_input)
model.summary()

# 학습 시작
history = model.fit(
    dataset,
    epochs=1,
    steps_per_epoch = encoded_inputs.shape[0] // batch_size,
    verbose=1
)

# 가중치 저장
model.save_weights("Cobra.weights.h5")
print("모델 가중치 저장 완료!")

def generate_text_topp(model, prompt, max_len=100, max_gen=98, p=0.9, temperature=0.8, min_len=20):
    model_input = text_to_ids(f"<start> {prompt} <sep>")
    model_input = model_input[:max_len]
    generated = list(model_input)
    for step in range(max_gen):
        if len(generated) > max_len:
            input_seq = generated[-max_len:]
        else:
            input_seq = generated
        input_padded = np.pad(input_seq, (0, max_len - len(input_seq)), constant_values=pad_id)
        input_tensor = tf.convert_to_tensor([input_padded])
        logits = model(input_tensor, training=False)
        next_token_logits = logits[0, len(input_seq) - 1].numpy()
        next_token_logits[end_id] -= 5.0
        next_token_logits[pad_id] -= 10.0
        probs = tf.nn.softmax(next_token_logits / temperature).numpy()
        sorted_indices = np.argsort(probs)[::-1]
        sorted_probs = probs[sorted_indices]
        cumulative_probs = np.cumsum(sorted_probs)
        cutoff = np.searchsorted(cumulative_probs, p)
        top_indices = sorted_indices[:cutoff + 1]
        top_probs = sorted_probs[:cutoff + 1]
        top_probs /= np.sum(top_probs)
        next_token_id = np.random.choice(top_indices, p=top_probs)
        if next_token_id == end_id and len(generated) >= min_len:
            break
        generated.append(int(next_token_id))
    return ids_to_text(generated)

print("\n\n===== 생성 결과 =====")  
print(generate_text_topp(model, "안녕", p=0.9))