首先，一个更简单的例子来理解MultiHeadAttention掩码。

# Crude Self attention implementation

query = tf.constant([[1], [2], [3], [4]], dtype=tf.float32)  #Shape([4, 1])

scores = tf.matmul(query, query, transpose_b=True) #Shape([4, 4])

# unnormalized, presoftmax score

上面是给定查询的attention scores。attention_mask用于防止注意此乐谱中的某些位置。因此，掩蔽维度应该与注意力得分维度相同。
假设我们决定上面示例中的当前令牌只需要关注其自身和下一个令牌，那么我们可以将掩码定义为：

mask = tf.constant([[1., 1., -np.inf, -np.inf],
        [-np.inf, 1., 1. ,-np.inf],
        [-np.inf, -np.inf, 1., 1.],
        [-np.inf, -np.inf, -np.inf, 1.]])

# apply mask on the score

scores = scores*mask

# softmax

scores = tf.nn.softmax(scores)

# scores, ( 0 indicates no attention)

[[0.26894143, 0.73105854, 0.        , 0.        ],
 [0.        , 0.11920292, 0.880797  , 0.        ],
 [0.        , 0.        , 0.04742587, 0.95257413],
 [0.        , 0.        , 0.        , 1.        ]]

# score weighted queries

value = tf.matmul(scores, query)

# value is a weighted average of the current and next token of ( [[1], [2], [3], [4]])

[[1.7310585], #weighted average of ([1], [2]) (current and next)
 [2.8807971],
 [3.9525743],
 [4.       ]]

批次中的每一项都可以有不同的掩膜吗？
是的，我能想到的一个用例是，当您对同一批次中的不同样品进行填充时，因此可以设置掩码以忽略这些填充。
您的特定情况：掩码必须是(batch_size, 4, 4)。对于批次中的每个项目，掩码可以是相同的。

batch_size = 5
query = keras.Input(shape=(4, 3))
mask_tensor = keras.Input(shape=(4, 4))

# keras layer

mha = keras.layers.MultiHeadAttention(num_heads=1, key_dim=3)
output = mha(query=query, value=query, attention_mask=mask_tensor, return_attention_scores=True)

# Create a model

model = keras.Model([query, mask_tensor], output)

# random query and mask. Note the mask needs to be (1:attention or 0:no attention)

queries = tf.random.normal(shape=(batch_size, 4, 3))
mask_data = tf.random.uniform(maxval=2, shape=(batch_size, 4, 4), dtype=tf.int32)

# calling the model

values, attn_weights = model.predict([queries, mask_data])

# attm_weights.shape

(5, 1, 4, 4)

经过一点研究和看了几个变压器模型的例子，这就解决了我的问题。
1.创建支持掩码的自定义TransformerBlock层
1.在TransformerBlock的call方法中添加一个mask参数，并对其进行整形。
1.在TransformerBlock之前增加一个Masking层
代码：

class TransformerBlock(layers.Layer):
    def __init__(self, head_size, num_heads, ff_dim, ff_dim2, rate=0.1):
        super().__init__()
        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=head_size)
        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
        self.dropout1 = layers.Dropout(rate)
        self.dropout2 = layers.Dropout(rate)
        self.conv1 = layers.Conv1D(filters=ff_dim, kernel_size=1, activation="relu")
        self.conv2 = layers.Conv1D(filters=ff_dim2, kernel_size=1)
        self.supports_masking = True

    def call(self, inputs, training, mask=None):
        padding_mask = None
        if mask is not None:
            padding_mask = tf.cast(mask[:, tf.newaxis, tf.newaxis, :], dtype="int32")

        out_norm1 = self.layernorm1(inputs, training=training)
        out_att = self.att(
            out_norm1, out_norm1, training=training, attention_mask=padding_mask
        )
        out_drop1 = self.dropout1(out_att, training=training)
        res = out_drop1 + inputs
        out_norm2 = self.layernorm2(res, training=training)
        out_conv1 = self.conv1(out_norm2, training=training)
        out_drop2 = self.dropout2(out_conv1, training=training)
        out_conv2 = self.conv2(out_drop2, training=training)
        return out_conv2 + res

def build_model(
    n_classes,
    input_shape,
    head_size,
    num_heads,
    ff_dim,
    num_transformer_blocks,
    mlp_units,
    dropout=0.0,
    mlp_dropout=0.0,
    mask=None,
) -> keras.Model:
    inputs = keras.Input(shape=input_shape)
    _x = inputs
    if mask is not None:
        _x = layers.Masking(mask_value=mask)(_x)
    for _ in range(num_transformer_blocks):
        _x = TransformerBlock(
            head_size,
            num_heads,
            ff_dim,
            inputs.shape[-1],
            dropout,
        )(_x)

    _x = layers.GlobalAveragePooling2D(data_format="channels_first")(_x)
    for dim in mlp_units:
        _x = layers.Dense(dim, activation="relu")(_x)
        _x = layers.Dropout(mlp_dropout)(_x)
    outputs = layers.Dense(n_classes, activation="softmax")(_x)
    return keras.Model(inputs, outputs)