请使用dace的工具箱完成上面的代码

好的，以下是使用Dace完成的代码：

import dace
import numpy as np

# 定义模型参数
num_layers = 12
d_model = 768
num_heads = 12
dff = 3072

# 定义输入和输出形状
B = 1
S = 128
N = S * B
input_shape = (B, S, d_model)
output_shape = (B, S, d_model)

# 定义计算图
@dace.program
def transformer_encoder(x_in: dace.float32[input_shape], training: dace.int32):
    # 计算注意力权重
    q = dace.define_local([B, S, num_heads, d_model // num_heads], dtype=dace.float32)
    k = dace.define_local([B, S, num_heads, d_model // num_heads], dtype=dace.float32)
    v = dace.define_local([B, S, num_heads, d_model // num_heads], dtype=dace.float32)
    attention = dace.define_local([B, num_heads, S, S], dtype=dace.float32)
    output = dace.define_local([B, S, num_heads, d_model // num_heads], dtype=dace.float32)
    for b in range(B):
        for i in range(num_heads):
            q[b, :, i, :] = x_in[b, :, i * (d_model // num_heads):(i + 1) * (d_model // num_heads)]
            k[b, :, i, :] = x_in[b, :, i * (d_model // num_heads):(i + 1) * (d_model // num_heads)]
            v[b, :, i, :] = x_in[b, :, i * (d_model // num_heads):(i + 1) * (d_model // num_heads)]

    for b in range(B):
        for i in range(num_heads):
            matmul_qk = np.matmul(q[b, :, i, :], np.transpose(k[b, :, i, :]))
            dk = np.sqrt(np.float32(d_model // num_heads))
            scaled_attention_logits = matmul_qk / dk
            attention[b, i, :, :] = np.transpose(np.exp(scaled_attention_logits - np.max(scaled_attention_logits)) /
                                                  np.sum(np.exp(scaled_attention_logits - np.max(scaled_attention_logits)),
                                                         axis=-1, keepdims=True))

            output[b, :, i, :] = np.matmul(attention[b, i, :, :], v[b, :, i, :])

    # 残差连接和层归一化
    ffn_output = dace.define_local([B, S, dff], dtype=dace.float32)
    for b in range(B):
        for i in range(num_heads):
            ffn_output[b, :, :] += output[b, :, i, :]
    ffn_output = np.reshape(ffn_output, (N, dff))
    ffn_output = dace.math.relu(dace.math.matmul(ffn_output, np.random.normal(size=(dff, d_model)).astype(np.float32)))
    ffn_output = np.reshape(ffn_output, (B, S, d_model))
    x_out = dace.math.add(x_in, ffn_output)
    x_out = dace.libraries.math.layer_norm(x_out, epsilon=1e-6)

    return x_out

# 测试计算图
x_in = np.random.normal(size=input_shape).astype(np.float32)
print("Input tensor:\n", x_in)
x_out = transformer_encoder(x_in, training=1)
print("Output tensor:\n", x_out)