self-attention layer abstract class. Self-attention layer takes input with size [b, s, h] and returns output of the same size.
| 48 | |
| 49 | |
| 50 | class SelfAttention(paddle.nn.Layer): |
| 51 | """self-attention layer abstract class. |
| 52 | |
| 53 | Self-attention layer takes input with size [b, s, h] |
| 54 | and returns output of the same size. |
| 55 | """ |
| 56 | |
| 57 | def __init__( |
| 58 | self, |
| 59 | hidden_size, |
| 60 | num_attention_heads, |
| 61 | layer_number, |
| 62 | fp16=True, |
| 63 | attention_softmax_in_fp32=True, |
| 64 | ): |
| 65 | super(SelfAttention, self).__init__() |
| 66 | self.hidden_size = hidden_size |
| 67 | self.num_attention_heads = num_attention_heads |
| 68 | self.fp16 = fp16 |
| 69 | self.attention_softmax_in_fp32 = attention_softmax_in_fp32 |
| 70 | self.layer_number = max(1, layer_number) |
| 71 | |
| 72 | assert self.hidden_size % self.num_attention_heads == 0 |
| 73 | self.hidden_size_per_attention_head = int(self.hidden_size // self.num_attention_heads) |
| 74 | |
| 75 | self.query = paddle.nn.Linear(self.hidden_size, self.hidden_size) |
| 76 | self.key = paddle.nn.Linear(self.hidden_size, self.hidden_size) |
| 77 | self.value = paddle.nn.Linear(self.hidden_size, self.hidden_size) |
| 78 | |
| 79 | self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) |
| 80 | self.softmax = paddle.nn.Softmax(axis=-1) |
| 81 | |
| 82 | self.dense = paddle.nn.Linear(self.hidden_size, self.hidden_size) |
| 83 | |
| 84 | def forward( |
| 85 | self, |
| 86 | hidden_states, |
| 87 | attention_mask, |
| 88 | layer_past=None, |
| 89 | get_key_value=False, |
| 90 | prompt_length=None, |
| 91 | context_length=None, |
| 92 | ): |
| 93 | # hidden_states: [sq, b, h] |
| 94 | |
| 95 | # ===================== |
| 96 | # Query, Key, and Value |
| 97 | # ===================== |
| 98 | |
| 99 | query_layer = self.query(hidden_states) |
| 100 | key_layer = self.key(hidden_states) |
| 101 | value_layer = self.value(hidden_states) |
| 102 | |
| 103 | new_query_layer_shape = query_layer.shape[:-1] + \ |
| 104 | [self.num_attention_heads, |
| 105 | self.hidden_size_per_attention_head] |
| 106 | query_layer = query_layer.reshape(new_query_layer_shape) |
| 107 |