mamba_text_generation.py

import torch
from mamba_block import MambaBlock
from mamba_config import MambaConfig
from mamba_layer import MambaLayer

# 創建一個Mamba配置
config = MambaConfig(
    hidden_size=512,
    num_layers=6,
    num_heads=8,
    intermediate_size=2048,
    max_position_embeddings=1024,
    rms_norm=False,
    residual_in_fp32=False,
    fused_add_norm=False,
)

# 創建一個Mamba模型
class MambaModel(torch.nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layers = torch.nn.ModuleList([MambaBlock(config, MambaLayer) for _ in range(config.num_layers)])
        self.norm = torch.nn.LayerNorm(config.hidden_size)

    def forward(self, hidden_states: torch.Tensor):
        residual = None
        for layer in self.layers:
            hidden_states, residual = layer(hidden_states, residual)
        hidden_states = self.norm(hidden_states + residual if residual is not None else hidden_states)
        return hidden_states

# 創建模型實例
mamba_model = MambaModel(config)
mamba_model.eval()

# Function to generate text from a given prompt using the Mamba model
def generate_text(prompt, model, max_length=50):
    # 這裡假設你的prompt已經被轉換為嵌入向量
    hidden_states = torch.randn(1, len(prompt), config.hidden_size)  # 假設你的輸入序列長度是len(prompt)

    with torch.no_grad():
        output = model(hidden_states)

    # 這裡你需要將模型輸出轉換為可讀的文本
    # 這只是一個示例，實際上你可能需要一個解碼器來將輸出轉換為文本
    generated_text = "這裡是生成的文本"  # 這裡應該是你的實際生成的文本

    return generated_text

# Function to generate text from a given prompt using the Mamba model
def generate_uncensored_text(prompt, max_length=50):
    mamba_text = generate_text(prompt, mamba_model, max_length)
    return mamba_text

# Example usage
prompt = "I want to generate some uncensored text."
uncensored_text = generate_uncensored_text(prompt)
print(uncensored_text)